{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T14:52:09Z","timestamp":1784299929560,"version":"3.55.0"},"reference-count":279,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2023,11,2]],"date-time":"2023-11-02T00:00:00Z","timestamp":1698883200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,11,2]],"date-time":"2023-11-02T00:00:00Z","timestamp":1698883200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Fundamental Research Funds for Central Universities of the Civil Aviation University of China","award":["3122021088"],"award-info":[{"award-number":["3122021088"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Deep neural networks (DNNs) have achieved great success in many object detection tasks. However, such DNNS-based large object detection models are generally computationally expensive and memory intensive. It is difficult to deploy them to devices with low memory resources or scenarios with high real-time requirements, which greatly limits their application and promotion. In recent years, many researchers have focused on compressing large object detection models without significantly degrading their performance, and have made great progress. Therefore, this paper presents a survey of object detection model compression techniques in recent years. Firstly, these compression techniques were divided into six categories: network pruning, lightweight network design, neural architecture search (NAS), low-rank decomposition, network quantization, and Knowledge distillation (KD) methods. For each category, we select some representative state-of-the-art methods and compare and analyze their performance on public datasets. After that, we discuss the application scenarios and future directions of model compression techniques. Finally, this paper is further concluded by analyzing the advantages and disadvantages of six types of model compression techniques.<\/jats:p>","DOI":"10.1007\/s11042-023-17192-x","type":"journal-article","created":{"date-parts":[[2023,11,2]],"date-time":"2023-11-02T05:13:34Z","timestamp":1698902014000},"page":"48165-48236","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["A survey of model compression strategies for object detection"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9427-1423","authenticated-orcid":false,"given":"Zonglei","family":"Lyu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tong","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuxi","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yilin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiren","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangyao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,11,2]]},"reference":[{"key":"17192_CR1","doi-asserted-by":"publisher","unstructured":"Heo B, Kim J, Yun S, Park H, Kwak N, Choi JY (2019) A comprehensive overhaul of feature distillation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision 2019:1921\u20131930. https:\/\/doi.org\/10.1109\/ICCV.2019.00201","DOI":"10.1109\/ICCV.2019.00201"},{"key":"17192_CR2","doi-asserted-by":"publisher","unstructured":"Ahn S, Hu SX, Damianou A, Lawrence ND, Dai Z (2019) Variational information distillation for knowledge transfer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2019:9163\u20139171. https:\/\/doi.org\/10.1109\/CVPR.2019.00938","DOI":"10.1109\/CVPR.2019.00938"},{"key":"17192_CR3","doi-asserted-by":"publisher","unstructured":"Gao S, Huang F, Pei J, Huang H (2020) Discrete model compression with resource constraint for deep neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2020:1899\u20131908. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00197","DOI":"10.1109\/CVPR42600.2020.00197"},{"key":"17192_CR4","doi-asserted-by":"publisher","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D et al (2015) Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015:1\u20139. https:\/\/doi.org\/10.1109\/CVPR.2015.7298594","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"17192_CR5","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/978-981-16-2543-5_4","volume":"1349","author":"MdA Feroz","year":"2022","unstructured":"Feroz MdA, Sultana M, Hasan MdR, Sarker A, Chakraborty P, Choudhury T (2022) Object detection and classification from a real-time video using SSD and YOLO models. In Computational Intelligence in Pattern Recognition, Advances in Intelligent Systems and Computing 1349:37\u201347. https:\/\/doi.org\/10.1007\/978-981-16-2543-5_4","journal-title":"In Computational Intelligence in Pattern Recognition, Advances in Intelligent Systems and Computing"},{"issue":"8","key":"17192_CR6","doi-asserted-by":"publisher","first-page":"2988","DOI":"10.3390\/s22082988","volume":"22","author":"A Vulli","year":"2022","unstructured":"Vulli A, Srinivasu PN, Sashank MSK, Shafi J, Choi J, Ijaz MF (2022) Fine-tuned DenseNet-169 for breast cancer metastasis prediction using FastAI and 1-cycle policy. Sensors (Basel, Switzerland) 22(8):2988. https:\/\/doi.org\/10.3390\/s22082988","journal-title":"Sensors (Basel, Switzerland)"},{"key":"17192_CR7","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/978-981-16-9447-9_9","volume":"281","author":"SM Basha","year":"2022","unstructured":"Basha SM, Ahmed ST, Al-Shammari NK (2022) A study on evaluating the performance of robot motion using gradient generalized artificial potential fields with obstacles. In Computational Intelligence in Data Mining, Systems and Technologies 281:113\u2013125. https:\/\/doi.org\/10.1007\/978-981-16-9447-9_9","journal-title":"In Computational Intelligence in Data Mining, Systems and Technologies"},{"key":"17192_CR8","doi-asserted-by":"publisher","unstructured":"Ahmed S, Guptha N, Fathima A, Ashwini S (2021) Multi-view feature clustering technique for detection and classification of human actions. In: Proceedings of the First International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICASISET. https:\/\/doi.org\/10.4108\/eai.16-5-2020.2304034","DOI":"10.4108\/eai.16-5-2020.2304034"},{"key":"17192_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/IJCNN52387.2021.9533792","volume":"2021","author":"SA de Aguiar","year":"2021","unstructured":"de Aguiar SA, Barros RC (2021) Model compression in object detection. In: Int Joint Conf Neural Netw (IJCNN) 2021:1\u20138.10. https:\/\/doi.org\/10.1109\/IJCNN52387.2021.9533792","journal-title":"Int Joint Conf Neural Netw (IJCNN)"},{"key":"17192_CR10","doi-asserted-by":"publisher","unstructured":"Cheng Y, Wang D, Zhou P, Tao Z (2017) A survey of model compression and acceleration for deep neural networks.  arXiv preprint arXiv:1710.09282. https:\/\/doi.org\/10.48550\/arXiv.1710.09282","DOI":"10.48550\/arXiv.1710.09282"},{"key":"17192_CR11","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/JPROC.2023.3238524","volume":"111","author":"Z Zou","year":"2019","unstructured":"Zou Z, Shi Z, Guo Y, Ye J (2019) Object detection in 20 years: a survey. Proceedings of the IEEE 111:257\u2013276. https:\/\/doi.org\/10.1109\/JPROC.2023.3238524","journal-title":"Proceedings of the IEEE"},{"key":"17192_CR12","unstructured":"LeCun Y, Denker JS, Solla SA (1990)\u00a0Optimal brain damage. In: advances\u00a0in neural information processing systems 2:598\u2013605"},{"key":"17192_CR13","first-page":"1135","volume":"1","author":"S Han","year":"2015","unstructured":"Han S, Pool J, Tran J, Dally W (2015a) Learning both weights and connections for efficient neural network. Adv Neural Inf Proces Syst 1:1135\u20131143","journal-title":"Adv Neural Inf Proces Syst"},{"key":"17192_CR14","doi-asserted-by":"publisher","unstructured":"Han S, Mao H, Dally WJ (2015b). Deep compression: compressing deep neural networks with pruning, trained quantization and huffman coding. In Proceedings of International Conference on Learning Representations, 2016. https:\/\/doi.org\/10.48550\/arXiv.1510.00149","DOI":"10.48550\/arXiv.1510.00149"},{"key":"17192_CR15","doi-asserted-by":"publisher","unstructured":"Lin Y, Han S, Mao H, Wang Y, Dally WJ (2017) Deep gradient compression: reducing the communication bandwidth for distributed training. In Proceedings of International Conference on Learning Representations, 2018. https:\/\/doi.org\/10.48550\/arXiv.1712.01887","DOI":"10.48550\/arXiv.1712.01887"},{"key":"17192_CR16","first-page":"2383","volume":"330","author":"G Li","year":"2018","unstructured":"Li G, Qian C, Jiang C, Lu X, Tang K (2018) Optimization based layer-wise magnitude-based pruning for DNN compression. IJCAI 330:2383\u20132389","journal-title":"IJCAI"},{"key":"17192_CR17","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/978-3-030-01237-3_12","volume":"11212","author":"T Zhang","year":"2018","unstructured":"Zhang T, Ye S, Zhang K, Tang J, Wen W, Fardad M, Wang Y (2018) A systematic dnn weight pruning framework using alternating direction method of multipliers. Proceedings of the European Conference on Computer Vision (ECCV) 11212:191\u2013207. https:\/\/doi.org\/10.1007\/978-3-030-01237-3_12","journal-title":"Proceedings of the European Conference on Computer Vision (ECCV)"},{"key":"17192_CR18","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1007\/978-3-030-01237-3_25","volume":"11212","author":"C Chen","year":"2018","unstructured":"Chen C, Tung F, Vedula N, Mori G (2018) Constraint-aware deep neural network compression. Proceedings of the European Conference on Computer Vision (ECCV) 11212:400\u2013415. https:\/\/doi.org\/10.1007\/978-3-030-01237-3_25","journal-title":"Proceedings of the European Conference on Computer Vision (ECCV)"},{"key":"17192_CR19","first-page":"10717","volume-title":"International Conference on Machine Learning","author":"W Wang","year":"2021","unstructured":"Wang W, Chen M, Zhao S, Chen L, Hu J, Liu H, Liu W (2021) ACTD Accelerate cnns from three dimensions: a comprehensive pruning framework. International Conference on Machine Learning, vol 2021. PMLR, pp 10717\u201310726"},{"key":"17192_CR20","doi-asserted-by":"publisher","unstructured":"Lee J, Park S, Mo S, Ahn S, Shin J (2020) Layer-adaptive sparsity for the magnitude-based pruning. In International Conference on Learning Representations, 2021. https:\/\/doi.org\/10.48550\/arXiv.2010.07611","DOI":"10.48550\/arXiv.2010.07611"},{"key":"17192_CR21","first-page":"1051","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems","author":"Z Liu","year":"2018","unstructured":"Liu Z, Xu J, Peng X, Xiong R (2018) Frequency-domain dynamic pruning for convolutional neural networks. Proceedings of the 32nd International Conference on Neural Information Processing Systems. pp 1051\u20131061"},{"key":"17192_CR22","doi-asserted-by":"publisher","first-page":"8532","DOI":"10.1109\/CVPR.2018.00890","volume":"2018","author":"MA Carreira-Perpin\u00e1n","year":"2018","unstructured":"Carreira-Perpin\u00e1n MA, Idelbayev Y (2018) \u201cLearning-compression\u201d algorithms for neural net pruning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2018:8532\u20138541. https:\/\/doi.org\/10.1109\/CVPR.2018.00890","journal-title":"In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"17192_CR23","doi-asserted-by":"publisher","first-page":"7765","DOI":"10.1109\/CVPR.2018.00810","volume":"2018","author":"A Mallya","year":"2018","unstructured":"Mallya A, Lazebnik S (2018) Packnet: Adding multiple tasks to a single network by iterative pruning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2018:7765\u20137773. https:\/\/doi.org\/10.1109\/CVPR.2018.00810","journal-title":"In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"17192_CR24","doi-asserted-by":"publisher","first-page":"1906","DOI":"10.1109\/CVPR42600.2020.00198","volume":"2020","author":"SJ Kwon","year":"2020","unstructured":"Kwon SJ, Lee D, Kim B, Kapoor P, Park B, Wei GY (2020) Structured compression by weight encryption for unstructured pruning and quantization. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2020:1906\u20131915. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00198","journal-title":"In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"17192_CR25","doi-asserted-by":"publisher","first-page":"6377","DOI":"10.5555\/3495724.3496259","volume":"535","author":"J Frankle","year":"2020","unstructured":"Frankle J, Dziugaite GK, Roy DM, Carbin M (2020) Pruning neural networks at initialization: why are we missing the mark? In Proceedings of International Conference on Learning Representations 535:6377\u20136389. https:\/\/doi.org\/10.5555\/3495724.3496259","journal-title":"In Proceedings of International Conference on Learning Representations"},{"key":"17192_CR26","unstructured":"Hu H, Peng R, Tai YW, Tang CK (2016). Network trimming: a data-driven neuron pruning approach towards efficient deep architectures. arXiv:1607.03250. https:\/\/arxiv.org\/abs\/1607.03250"},{"key":"17192_CR27","doi-asserted-by":"publisher","first-page":"1387","DOI":"10.48550\/arXiv.1608.04493","volume":"29","author":"Y Guo","year":"2016","unstructured":"Guo Y, Yao A, Chen Y (2016) Dynamic network surgery for efficient dnns. Advances in Neural Information Processing Systems 29:1387\u20131395. https:\/\/doi.org\/10.48550\/arXiv.1608.04493","journal-title":"Advances in Neural Information Processing Systems"},{"key":"17192_CR28","doi-asserted-by":"publisher","unstructured":"Yu R, Li A, Chen CF, Lai JH, Morariu VI, Han X, Davis LS (2018) Nisp: pruning networks using neuron importance score propagation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 9194\u20139203. https:\/\/doi.org\/10.1109\/CVPR.2018.00958","DOI":"10.1109\/CVPR.2018.00958"},{"key":"17192_CR29","doi-asserted-by":"publisher","unstructured":"Huang Z, Wang N (2018) Data-driven sparse structure selection for deep neural networks. In Proceedings of the European conference on computer vision (ECCV) 11220:317\u2013334. https:\/\/doi.org\/10.1007\/978-3-030-01270-0_19","DOI":"10.1007\/978-3-030-01270-0_19"},{"key":"17192_CR30","doi-asserted-by":"publisher","unstructured":"Molchanov P, Mallya A, Tyree S, Frosio I, Kautz J (2019) Importance estimation for neural network pruning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition,pp  11264\u201311272. https:\/\/doi.org\/10.1109\/CVPR.2019.01152","DOI":"10.1109\/CVPR.2019.01152"},{"key":"17192_CR31","doi-asserted-by":"publisher","unstructured":"Khakzar A, Baselizadeh S, Khanduja S, Rupprecht C, Kim ST, Navab N (2021) Neural response interpretation through the lens of critical pathways. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 13523-13533. https:\/\/doi.org\/10.1109\/CVPR46437.2021.01332","DOI":"10.1109\/CVPR46437.2021.01332"},{"key":"17192_CR32","doi-asserted-by":"publisher","unstructured":"Wang H, Qin C, Zhang Y, Fu Y (2020) Neural pruning via growing regularization. In International Conference on Learning Representations 2021. https:\/\/doi.org\/10.48550\/arXiv.2012.09243","DOI":"10.48550\/arXiv.2012.09243"},{"issue":"04","key":"17192_CR33","doi-asserted-by":"crossref","first-page":"4876","DOI":"10.1609\/aaai.v34i04.5924","volume":"34","author":"N Liu","year":"2020","unstructured":"Liu N, Ma X, Xu Z, Wang Y, Tang J, Ye J (2020) AutoCompress An automatic DNN structured pruning framework for ultra-high compression rates. In Proceedings of the AAAI Conference on Artificial Intelligence 34(04):4876\u20134883","journal-title":"In Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"04","key":"17192_CR34","doi-asserted-by":"crossref","first-page":"5117","DOI":"10.1609\/aaai.v34i04.5954","volume":"34","author":"X Ma","year":"2020","unstructured":"Ma X, Guo FM, Niu W, Lin X, Tang J, Ma K, Wang Y (2020) Pconv: the missing but desirable sparsity in dnn weight pruning for real-time execution on mobile devices. In Proceedings of the AAAI Conference on Artificial Intelligence 34(04):5117\u20135124","journal-title":"In Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"17192_CR35","doi-asserted-by":"publisher","unstructured":"Ao R, Tao Z, Yuhao W, Sheng L, Peiyan D, Yen-kuang C,.et al (2020). DARB: a density-adaptive regular-block pruning for deep neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence 34(04):5495\u20135502. https:\/\/doi.org\/10.1609\/aaai.v34i04.6000","DOI":"10.1609\/aaai.v34i04.6000"},{"key":"17192_CR36","unstructured":"Wen W, Wu C, Wang Y, Chen Y, Li H (2016) Learning structured sparsity in deep neural networks. In Advances in Neural Information Processing Systems, pp 2082\u20132090"},{"key":"17192_CR37","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1007\/978-3-319-46493-0_40","volume-title":"European Conference on Computer Vision","author":"H Zhou","year":"2016","unstructured":"Zhou H, Alvarez JM, Porikli F (2016) Less is more: Towards compact cnns. European Conference on Computer Vision, vol 9908. Springer, Cham, pp 662\u2013677. https:\/\/doi.org\/10.1007\/978-3-319-46493-0_40"},{"key":"17192_CR38","unstructured":"Alvarez JM, Salzmann M (2016) Learning the number of neurons in deep networks. In Advances in Neural Information Processing Systems, pp 2270\u20132278"},{"key":"17192_CR39","first-page":"3958","volume":"70","author":"J Yoon","year":"2017","unstructured":"Yoon J, Hwang SJ (2017) Combined group and exclusive sparsity for deep neural networks. In International Conference on Machine Learning (PMLR) 70:3958\u20133966","journal-title":"In International Conference on Machine Learning (PMLR)"},{"key":"17192_CR40","doi-asserted-by":"publisher","unstructured":"Li H, Kadav A, Durdanovic I, Samet H, Graf HP (2017) Pruning filters for efficient convnets. In: ICLR 2017: International conference on learning representations 2017. https:\/\/doi.org\/10.48550\/arXiv.1608.08710","DOI":"10.48550\/arXiv.1608.08710"},{"key":"17192_CR41","doi-asserted-by":"publisher","first-page":"2554","DOI":"10.1109\/CVPR.2016.280","volume":"2016","author":"V Lebedev","year":"2016","unstructured":"Lebedev V, Lempitsky V (2016) Fast convnets using group-wise brain damage. Proc IEEE Conf Comput Vis Pattern Recognit 2016:2554\u20132564. https:\/\/doi.org\/10.1109\/CVPR.2016.280","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"17192_CR42","doi-asserted-by":"publisher","unstructured":"He Y, Zhang X, Sun J (2017) Channel pruning for accelerating very deep neural networks. In: Proceedings of the IEEE International Conference on Computer Vision 2017:1398\u20131406. https:\/\/doi.org\/10.1109\/ICCV.2017.155","DOI":"10.1109\/ICCV.2017.155"},{"key":"17192_CR43","first-page":"2575","volume":"28","author":"DP Kingma","year":"2015","unstructured":"Kingma DP, Salimans T, Welling M (2015) Variational dropout and the local reparameterization trick. In Advances in Neural Information Processing Systems (NIPS) 28:2575\u20132583","journal-title":"In Advances in Neural Information Processing Systems (NIPS)"},{"key":"17192_CR44","doi-asserted-by":"publisher","unstructured":"Louizos C, Welling M, Kingma DP (2017) Learning sparse neural networks through L0 regularization. arXiv:1712.01312.  https:\/\/doi.org\/10.48550\/arXiv.1712.01312","DOI":"10.48550\/arXiv.1712.01312"},{"key":"17192_CR45","first-page":"2498","volume":"70","author":"D Molchanov","year":"2017","unstructured":"Molchanov D, Ashukha A, Vetrov D (2017) Variational dropout sparsifies deep neural networks. In International Conference on Machine Learning (PMLR) 70:2498\u20132507","journal-title":"In International Conference on Machine Learning (PMLR)"},{"key":"17192_CR46","unstructured":"Neklyudov K, Molchanov D, Ashukha A, Vetrov D (2017) Structured bayesian pruning via log-normal multiplicative noise. In Advances in Neural Information Processing Systems, pp 6778\u20136787"},{"key":"17192_CR47","doi-asserted-by":"publisher","unstructured":"Lemaire C, Achkar A, Jodoin PM (2019) Structured pruning of neural networks with budget-aware regularization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2019:9108\u20139116. https:\/\/doi.org\/10.1109\/CVPR.2019.00932","DOI":"10.1109\/CVPR.2019.00932"},{"key":"17192_CR48","doi-asserted-by":"publisher","unstructured":"Louizos C, Ullrich K, Welling M (2017) Bayesian compression for deep learning. In: Proceedings of the 31st International Conference on Neural Information Processing Systems 30:3290-3300. https:\/\/doi.org\/10.48550\/arXiv.1705.08665","DOI":"10.48550\/arXiv.1705.08665"},{"key":"17192_CR49","doi-asserted-by":"publisher","unstructured":"Molchanov P, Tyree S, Karras T, Aila T, Kautz J (2016) Pruning convolutional neural networks for resource efficient inference. In: Proceedings of International Conference on Learning Representations 2017. https:\/\/doi.org\/10.48550\/arXiv.1611.06440","DOI":"10.48550\/arXiv.1611.06440"},{"key":"17192_CR50","unstructured":"Lin J, Rao Y, Lu J, Zhou J (2017) Runtime neural pruning. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, 30:2178\u20132188"},{"key":"17192_CR51","doi-asserted-by":"publisher","unstructured":"Ding X, Ding G, Han J, Tang S (2018) Auto-balanced filter pruning for efficient convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence 32(1):6797\u20136804. https:\/\/doi.org\/10.1609\/aaai.v32i1.12262","DOI":"10.1609\/aaai.v32i1.12262"},{"key":"17192_CR52","first-page":"8867","volume":"32","author":"X Ding","year":"2019","unstructured":"Ding X, Ding G, Zhou X, Guo Y, Han J, Liu J (2019) Global sparse momentum sgd for pruning very deep neural networks. Adv Neural Inf Processing Syst 32:8867","journal-title":"Adv Neural Inf Processing Syst"},{"key":"17192_CR53","doi-asserted-by":"crossref","unstructured":"Lin S, Ji R, Li Y, Wu Y, Huang F, Zhang B (2018) Accelerating convolutional networks via global & dynamic filter pruning. In IJCAI, pp 2425\u20132432","DOI":"10.24963\/ijcai.2018\/336"},{"key":"17192_CR54","doi-asserted-by":"publisher","first-page":"2009","DOI":"10.1109\/CVPR42600.2020.00208","volume":"2020","author":"Y He","year":"2020","unstructured":"He Y, Ding Y, Liu P, Zhu L, Zhang H, Yang Y (2020) Learning filter pruning criteria for deep convolutional neural networks acceleration. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2020:2009\u20132018. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00208","journal-title":"In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"17192_CR55","doi-asserted-by":"publisher","first-page":"14913","DOI":"10.1109\/CVPR46437.2021.01467","volume":"2021","author":"Z Wang","year":"2021","unstructured":"Wang Z, Li C, Wang X (2021) Convolutional neural network pruning with structural redundancy reduction. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2021:14913\u201314922. https:\/\/doi.org\/10.1109\/CVPR46437.2021.01467","journal-title":"In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"17192_CR56","first-page":"5018","volume":"1","author":"Y Tang","year":"2021","unstructured":"Tang Y, Wang Y, Xu Y, Deng Y, Xu C, Tao D, Xu C (2021) Manifold regularized dynamic network pruning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 1:5018\u20135028","journal-title":"In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"issue":"3","key":"17192_CR57","first-page":"2495","volume":"35","author":"X Ruan","year":"2021","unstructured":"Ruan X, Liu Y, Li B, Yuan C, Hu W (2021) DPFPS: dynamic and progressive filter pruning for compressing convolutional neural networks from scratch. Proc AAAI Conf Artif Intell 35(3):2495\u20132503","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"17192_CR58","first-page":"10149","volume":"2018","author":"C Lin","year":"2018","unstructured":"Lin C, Zhong Z, Wu W, Yan J (2018) Synaptic strength for convolutional neural network. Adv Neural Inf Processing Syst 2018:10149\u201310158","journal-title":"Adv Neural Inf Processing Syst"},{"key":"17192_CR59","doi-asserted-by":"publisher","unstructured":"Luo JH, Wu J (2020) Neural network pruning with residual-connections and limited-data. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2020:1458\u20131467. https:\/\/doi.org\/10.48550\/arXiv.1911.08114","DOI":"10.48550\/arXiv.1911.08114"},{"issue":"04","key":"17192_CR60","doi-asserted-by":"publisher","first-page":"5972","DOI":"10.1609\/aaai.v34i04.6058","volume":"34","author":"Y Tang","year":"2020","unstructured":"Tang Y, You S, Xu C, Han J, Qian C, Shi B, Zhang C (2020) Reborn filters: pruning convolutional neural networks with limited data. In Proc AAAI Conf Artif Intell 34(04):5972\u20135980. https:\/\/doi.org\/10.1609\/aaai.v34i04.6058","journal-title":"In Proc AAAI Conf Artif Intell"},{"key":"17192_CR61","doi-asserted-by":"publisher","first-page":"4943","DOI":"10.1109\/CVPR.2019.00508","volume":"2019","author":"X Ding","year":"2019","unstructured":"Ding X, Ding G, Guo Y, Han J (2019) Centripetal sgd for pruning very deep convolutional networks with complicated structure. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2019:4943\u20134953. https:\/\/doi.org\/10.1109\/CVPR.2019.00508","journal-title":"In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"17192_CR62","unstructured":"Liu Z, Sun M, Zhou T, Huang G, Darrell T (2018) Rethinking the value of network pruning. In Proceedings of International Conference on Learning Representations 2019"},{"key":"17192_CR63","first-page":"1607","volume":"1","author":"X Ding","year":"2019","unstructured":"Ding X, Ding G, Guo Y, Han J, Yan C (2019) Approximated oracle filter pruning for destructive cnn width optimization. In International Conference on Machine Learning 1:1607\u20131616 (PMLR)","journal-title":"In International Conference on Machine Learning"},{"key":"17192_CR64","doi-asserted-by":"publisher","unstructured":"He Y, Kang G, Dong X, Fu Y, Yang Y (2018) Soft filter pruning for accelerating deep convolutional neural networks. International Joint Conference on Artificial Intelligence (IJCAI) 2234\u20132240. https:\/\/doi.org\/10.24963\/ijcai.2018\/309","DOI":"10.24963\/ijcai.2018\/309"},{"key":"17192_CR65","doi-asserted-by":"publisher","unstructured":"Li Y, Lin S, Zhang B, Liu J, Doermann D, Wu Y, ... Ji R (2019) Exploiting kernel sparsity and entropy for interpretable CNN compression. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2019:2800\u20132809. https:\/\/doi.org\/10.1109\/CVPR.2019.00291","DOI":"10.1109\/CVPR.2019.00291"},{"key":"17192_CR66","doi-asserted-by":"publisher","first-page":"2790","DOI":"10.1109\/CVPR.2019.00290","volume":"2019","author":"S Lin","year":"2019","unstructured":"Lin S, Ji R, Yan C, Zhang B, Cao L, Ye Q, Doermann D (2019) Towards optimal structured cnn pruning via generative adversarial learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2019:2790\u20132799. https:\/\/doi.org\/10.1109\/CVPR.2019.00290","journal-title":"In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"17192_CR67","doi-asserted-by":"publisher","unstructured":"Singh P, Verma VK, Rai P, Namboodiri VP (2019a) Play and prune: adaptive filter pruning for deep model compression. In IJCAI, pp 3460\u20133466. https:\/\/doi.org\/10.24963\/ijcai.2019\/480","DOI":"10.24963\/ijcai.2019\/480"},{"key":"17192_CR68","doi-asserted-by":"publisher","unstructured":"Enderich L, Timm F, Burgard W (2021) Holistic filter pruning for efficient deep neural networks. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp 2596\u20132605. https:\/\/doi.org\/10.1109\/WACV48630.2021.00264","DOI":"10.1109\/WACV48630.2021.00264"},{"key":"17192_CR69","doi-asserted-by":"publisher","unstructured":"Luo JH, Wu J, Lin W (2017) Thinet: A filter level pruning method for deep neural network compression. In Proceedings of the IEEE international conference on computer vision 2017:5058. https:\/\/doi.org\/10.1109\/ICCV.2017.541","DOI":"10.1109\/ICCV.2017.541"},{"key":"17192_CR70","doi-asserted-by":"publisher","unstructured":"Li T, Wu B, Yang Y, Fan Y, Zhang Y, Liu W (2019) Compressing convolutional neural networks via factorized convolutional filters. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2019:3977\u20133986. https:\/\/doi.org\/10.1109\/CVPR.2019.00410","DOI":"10.1109\/CVPR.2019.00410"},{"issue":"191","key":"17192_CR71","first-page":"2133","volume":"32","author":"Z You","year":"2019","unstructured":"You Z, Yan K, Ye J, Ma M, Wang P (2019) Gate decorator: global filter pruning method for accelerating deep convolutional neural networks. Adv Neural Inf Proces Syst 32(191):2133\u20132144","journal-title":"Adv Neural Inf Proces Syst"},{"key":"17192_CR72","doi-asserted-by":"publisher","unstructured":"He Y, Liu P, Wang Z, Hu Z, Yang Y (2019) Filter pruning via geometric median for deep convolutional neural networks acceleration. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2019:4340\u20134349. https:\/\/doi.org\/10.1109\/CVPR.2019.00447","DOI":"10.1109\/CVPR.2019.00447"},{"key":"17192_CR73","first-page":"947","volume":"29","author":"M Figurnov","year":"2016","unstructured":"Figurnov M, Ibraimova A, Vetrov DP, Kohli P (2016) Perforatedcnns: acceleration through elimination of redundant convolutions. In Adv Neural Inf Processing Syst 29:947\u2013955","journal-title":"In Adv Neural Inf Processing Syst"},{"key":"17192_CR74","doi-asserted-by":"publisher","unstructured":"Singh P, Verma VK, Rai P, Namboodiri VP (2019b) Hetconv: heterogeneous kernel-based convolutions for deep cnns. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2019:4835\u20134844. https:\/\/doi.org\/10.1109\/CVPR.2019.00497","DOI":"10.1109\/CVPR.2019.00497"},{"issue":"11","key":"17192_CR75","first-page":"10227","volume":"35","author":"X Wang","year":"2021","unstructured":"Wang X, Stella XY (2021) Tied Block Convolution: Leaner and Better CNNs with Shared Thinner Filters. Proc AAAI Conf Artif Intell 35(11):10227\u201310235","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"17192_CR76","doi-asserted-by":"publisher","unstructured":"Liu Z, Li J, Shen Z, Huang G, Yan S, Zhang C (2017) Learning efficient convolutional networks through network slimming. In Proceedings of the IEEE International Conference on Computer Vision 2017:2736\u20132744. https:\/\/doi.org\/10.1109\/ICCV.2017.298","DOI":"10.1109\/ICCV.2017.298"},{"key":"17192_CR77","first-page":"7021","volume":"139","author":"L Liu","year":"2021","unstructured":"Liu L, Zhang S, Kuang Z, Zhou A, Xue JH, Wang X,... Zhang W, (2021) Group fisher pruning for practical network compression. In International Conference on Machine Learning 139:7021\u20137032","journal-title":"In International Conference on Machine Learning"},{"key":"17192_CR78","doi-asserted-by":"publisher","unstructured":"Ding X, Hao T, Tan J, Liu J, Han J, Guo Y, Ding G (2021) ResRep: lossless cnn pruning via decoupling remembering and forgetting. In Proceedings of the IEEE\/CVF International Conference on Computer Vision 2021:4490\u20134500. https:\/\/doi.org\/10.1109\/ICCV48922.2021.00447","DOI":"10.1109\/ICCV48922.2021.00447"},{"key":"17192_CR79","doi-asserted-by":"publisher","unstructured":"Hu Y, Sun S, Li J, Wang X, Gu Q (2018) A novel channel pruning method for deep neural network compression. arXiv preprint arXiv:1805.11394. https:\/\/doi.org\/10.48550\/arXiv.1805.11394","DOI":"10.48550\/arXiv.1805.11394"},{"key":"17192_CR80","unstructured":"Zhuang Z, Tan M, Zhuang B, Liu J, Guo Y, Wu Q, Zhu J (2018) Discrimination-aware channel pruning for deep neural networks. In Advances in Neural Information Processing Systems 31:883\u2013894."},{"key":"17192_CR81","first-page":"5113","volume":"1","author":"H Peng","year":"2019","unstructured":"Peng H, Wu J, Chen S, Huang J (2019) Collaborative channel pruning for deep networks. In International Conference on Machine Learning 1:5113\u20135122 (PMLR)","journal-title":"In International Conference on Machine Learning"},{"key":"17192_CR82","doi-asserted-by":"publisher","unstructured":"Zhao C, Ni B, Zhang J, Zhao Q, Zhang W, Tian Q (2019) Variational convolutional neural network pruning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2019:2775-2784. https:\/\/doi.org\/10.1109\/CVPR.2019.00289","DOI":"10.1109\/CVPR.2019.00289"},{"issue":"07","key":"17192_CR83","doi-asserted-by":"crossref","first-page":"12273","DOI":"10.1609\/aaai.v34i07.6910","volume":"34","author":"Y Wang","year":"2020","unstructured":"Wang Y, Zhang X, Xie L, Zhou J, Su H, Zhang B, Hu X (2020a) Pruning from scratch. In Proceedings of the AAAI Conference on Artificial Intelligence 34(07):12273\u201312280","journal-title":"In Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"04","key":"17192_CR84","first-page":"6299","volume":"34","author":"Y Wang","year":"2020","unstructured":"Wang Y, Zhang X, Hu X, Zhang B, Su H (2020b) Dynamic Network Pruning with Interpretable Layerwise Channel Selection. Proc AAAI Conf Artif Intell 34(04):6299\u20136306","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"17192_CR85","doi-asserted-by":"publisher","unstructured":"Li Y, Lin S, Liu J, Ye Q, Wang M, Chao F,... Ji R (2021) Towards compact cnns via collaborative compression. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2021:6434\u20136443. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00637","DOI":"10.1109\/CVPR46437.2021.00637"},{"key":"17192_CR86","unstructured":"Kang M, Han B (2020) Operation-aware soft channel pruning using differentiable masks. In International Conference on Machine Learning 119:5122\u20135131"},{"issue":"07","key":"17192_CR87","first-page":"10885","volume":"34","author":"J Guo","year":"2020","unstructured":"Guo J, Ouyang W, Xu D (2020) Channel pruning guided by classification loss and feature importance. Proc AAAI Conf Artif Intell 34(07):10885\u201310892","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"17192_CR88","doi-asserted-by":"publisher","unstructured":"Guo J, Ouyang W, Xu D (2020b) Multi-dimensional pruning: a unified framework for model compression. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2020:1505\u20131514. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00158","DOI":"10.1109\/CVPR42600.2020.00158"},{"key":"17192_CR89","doi-asserted-by":"publisher","unstructured":"Gao S, Huang F, Cai W, Huang H (2021) Network pruning via performance maximization. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2021:9266\u20139276. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00915","DOI":"10.1109\/CVPR46437.2021.00915"},{"issue":"9","key":"17192_CR90","first-page":"8021","volume":"35","author":"D Joo","year":"2021","unstructured":"Joo D, Yi E, Baek S, Kim J (2021) Linearly Replaceable Filters for Deep Network Channel Pruning. Proc AAAI Conf Artif Intell 35(9):8021\u20138029","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"17192_CR91","doi-asserted-by":"publisher","unstructured":"Hou Z, Qin M, Sun F, Ma X, Yuan K, Xu Y, Chen Y-K, Jin R, Xie Y, Kung S-Y, (2022) CHEX: channel exploration for cnn model compression. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2022:12277\u201312288. https:\/\/doi.org\/10.1109\/CVPR52688.2022.01197","DOI":"10.1109\/CVPR52688.2022.01197"},{"key":"17192_CR92","doi-asserted-by":"publisher","unstructured":"Poliakov E et al (2022) Model compression via structural pruning and feature distillation for accurate multi-spectral object detection on edge-devices. 2022 IEEE International Conference on Multimedia and Expo (ICME). pp 1\u20136. https:\/\/doi.org\/10.1109\/ICME52920.2022.9859994","DOI":"10.1109\/ICME52920.2022.9859994"},{"key":"17192_CR93","unstructured":"Dong X, Chen S, Pan SJ (2017) Learning to prune deep neural networks via layer-wise optimal brain surgeon. Adv Neural Inf Processing Syst 30:4857\u20134867."},{"key":"17192_CR94","doi-asserted-by":"publisher","first-page":"6071","DOI":"10.1109\/CVPR.2017.643","volume":"2017","author":"TJ Yang","year":"2017","unstructured":"Yang TJ, Chen YH, Sze V (2017) Designing energy-efficient convolutional neural networks using energy-aware pruning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017:6071\u20136079. https:\/\/doi.org\/10.1109\/CVPR.2017.643","journal-title":"In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"17192_CR95","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1007\/978-3-030-01249-6_18","volume":"11214","author":"TJ Yang","year":"2018","unstructured":"Yang TJ, Howard A, Chen B, Zhang X, Go A, Sandler M, Adam H (2018) Netadapt: platform-aware neural network adaptation for mobile applications. In Proceedings of the European Conference on Computer Vision (ECCV) 11214:289\u2013304. https:\/\/doi.org\/10.1007\/978-3-030-01249-6_18","journal-title":"In Proceedings of the European Conference on Computer Vision (ECCV)"},{"issue":"276","key":"17192_CR96","first-page":"2943","volume":"119","author":"U Evci","year":"2020","unstructured":"Evci U, Gale T, Menick J, Castro PS, Elsen E (2020) Rigging the lottery: making all tickets winners. In International Conference on Machine Learning 119(276):2943\u20132952","journal-title":"In International Conference on Machine Learning"},{"key":"17192_CR97","doi-asserted-by":"publisher","unstructured":"Hayou S, Ton JF, Doucet A, Teh YW (2020) Robust pruning at initialization. arXiv preprint arXiv:2002.08797. https:\/\/doi.org\/10.48550\/arXiv.2002.08797","DOI":"10.48550\/arXiv.2002.08797"},{"key":"17192_CR98","doi-asserted-by":"publisher","unstructured":"Le DH, Hua BS (2020) Network pruning that matters: a case study on retraining variants. arXiv preprint arXiv:2105.03193. https:\/\/doi.org\/10.48550\/arXiv.2105.03193","DOI":"10.48550\/arXiv.2105.03193"},{"key":"17192_CR99","doi-asserted-by":"publisher","unstructured":"Liu S, Chen T, Chen X, Shen Li, Mocanu D, Wang Z, Pechenizkiy M (2022) The unreasonable effectiveness of random pruning: return of the most naive baseline for sparse training. arXiv preprint arXiv:2202.02643.  https:\/\/doi.org\/10.48550\/arXiv.2202.02643","DOI":"10.48550\/arXiv.2202.02643"},{"key":"17192_CR100","doi-asserted-by":"crossref","unstructured":"Hu P, Peng X, Zhu H, Aly MMS, Lin J (2021) OPQ: compressing deep neural networks with one-shot pruning-quantization. In Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), Vancouver, VN, Canada 35(9):7780\u20137788","DOI":"10.1609\/aaai.v35i9.16950"},{"key":"17192_CR101","doi-asserted-by":"publisher","unstructured":"Tiwari R, Bamba U, Chavan A, Gupta D (2020) ChipNet: Budget-aware pruning with heaviside continuous approximations. In International Conference on Learning Representations 2021. arXiv preprint  arXiv:2102.07156. https:\/\/doi.org\/10.48550\/arXiv.2102.07156","DOI":"10.48550\/arXiv.2102.07156"},{"issue":"8","key":"17192_CR102","first-page":"6974","volume":"35","author":"X Chang","year":"2021","unstructured":"Chang X, Li Y, Oymak S, Thrampoulidis C (2021) Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks. Proc AAAI Conf Artif Intell 35(8):6974\u20136983","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"17192_CR103","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/CVPR.2015.7298594","volume":"2015","author":"C Szegedy","year":"2015","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D,... Rabinovich A, (2015) Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition 2015:1\u20139. https:\/\/doi.org\/10.1109\/CVPR.2015.7298594","journal-title":"In Proceedings of the IEEE conference on computer vision and pattern recognition"},{"key":"17192_CR104","first-page":"448","volume":"37","author":"S Ioffe","year":"2015","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In International conference on machine learning 37:448\u2013456","journal-title":"In International conference on machine learning"},{"key":"17192_CR105","doi-asserted-by":"publisher","first-page":"2818","DOI":"10.1109\/CVPR.2016.308","volume":"2016","author":"C Szegedy","year":"2016","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In Proc IEEE Conf Comput Vis Pattern Recognit 2016:2818\u20132826. https:\/\/doi.org\/10.1109\/CVPR.2016.308","journal-title":"In Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"17192_CR106","doi-asserted-by":"publisher","first-page":"1800","DOI":"10.1109\/CVPR.2017.195","volume":"2017","author":"F Chollet","year":"2017","unstructured":"Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In Proc IEEE Conf Comput Vis Pattern Recognit 2017:1800\u20131807. https:\/\/doi.org\/10.1109\/CVPR.2017.195","journal-title":"In Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"17192_CR107","doi-asserted-by":"publisher","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T,... Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861. https:\/\/doi.org\/10.48550\/arXiv.1704.04861","DOI":"10.48550\/arXiv.1704.04861"},{"key":"17192_CR108","doi-asserted-by":"publisher","first-page":"4510","DOI":"10.1109\/CVPR.2018.00474","volume":"2018","author":"M Sandler","year":"2018","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv 2: inverted residuals and linear bottlenecks. In Proc IEEE Conf Comput Vis Pattern Recognit 2018:4510\u20134520. https:\/\/doi.org\/10.1109\/CVPR.2018.00474","journal-title":"In Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"17192_CR109","doi-asserted-by":"publisher","unstructured":"Yu G, Chang Q, Lv W, Xu C, Cui C, Ji W,... Ma Y (2021) PP-PicoDet: a better real-time object detector on mobile devices. arXiv preprint arXiv:2111.00902. https:\/\/doi.org\/10.48550\/arXiv.2111.00902","DOI":"10.48550\/arXiv.2111.00902"},{"key":"17192_CR110","unstructured":"Tan M, Le QV (2019b) Mixconv: mixed depthwise convolutional kernels. In British Machine Vision Conference (BMVC), 33(116):1\u201313. https:\/\/dx.doi.org\/10.5244\/C.33.116"},{"key":"17192_CR111","doi-asserted-by":"publisher","unstructured":"Mehta S, Rastegari M, Shapiro L, Hajishirzi H (2019) Espnetv2: a light-weight, power efficient, and general purpose convolutional neural network. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2019:9182\u20139192. https:\/\/doi.org\/10.1109\/CVPR.2019.00941","DOI":"10.1109\/CVPR.2019.00941"},{"key":"17192_CR112","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1109\/CVPR.2016.90","volume":"2016","author":"K He","year":"2016","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In Proc IEEE Conf Comput Vis Pattern Recognit 2016:770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","journal-title":"In Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"17192_CR113","doi-asserted-by":"publisher","first-page":"5987","DOI":"10.1109\/CVPR.2017.634","volume":"2017","author":"S Xie","year":"2017","unstructured":"Xie S, Girshick R, Doll\u00e1r P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In Proc IEEE Conf Comput Vis Pattern Recognit 2017:5987\u20135995. https:\/\/doi.org\/10.1109\/CVPR.2017.634","journal-title":"In Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"17192_CR114","doi-asserted-by":"publisher","first-page":"2261","DOI":"10.1109\/CVPR.2017.243","volume":"2017","author":"G Huang","year":"2017","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In Proc IEEE Conf Comput Vis Pattern Recognit 2017:2261\u20132269. https:\/\/doi.org\/10.1109\/CVPR.2017.243","journal-title":"In Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"17192_CR115","doi-asserted-by":"publisher","first-page":"2752","DOI":"10.1109\/CVPR.2018.00291","volume":"2018","author":"G Huang","year":"2018","unstructured":"Huang G, Liu S, Van der Maaten L, Weinberger KQ (2018) Condensenet: An efficient densenet using learned group convolutions. In Proc IEEE Conf Comput Vis Pattern Recognit 2018:2752\u20132761. https:\/\/doi.org\/10.1109\/CVPR.2018.00291","journal-title":"In Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"17192_CR116","doi-asserted-by":"publisher","first-page":"4383","DOI":"10.1109\/ICCV.2017.469","volume":"2017","author":"T Zhang","year":"2017","unstructured":"Zhang T, Qi GJ, Xiao B, Wang J (2017) Interleaved group convolutions. In Proceedings of the IEEE international conference on computer vision 2017:4383\u20134392. https:\/\/doi.org\/10.1109\/ICCV.2017.469","journal-title":"In Proceedings of the IEEE international conference on computer vision"},{"key":"17192_CR117","doi-asserted-by":"publisher","first-page":"8847","DOI":"10.1109\/CVPR.2018.00922","volume":"2018","author":"G Xie","year":"2018","unstructured":"Xie G, Wang J, Zhang T, Lai J, Hong R, Qi GJ (2018) Interleaved structured sparse convolutional neural networks. In Proc IEEE Conf Comput Vis Pattern Recognit 2018:8847\u20138856. https:\/\/doi.org\/10.1109\/CVPR.2018.00922","journal-title":"In Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"17192_CR118","doi-asserted-by":"publisher","unstructured":"Sun K, Li M, Liu D, Wang J (2018) Igcv3: interleaved low-rank group convolutions for efficient deep neural networks. In British Machine Vision Conference (BMVC). https:\/\/doi.org\/10.48550\/arXiv.1806.00178","DOI":"10.48550\/arXiv.1806.00178"},{"key":"17192_CR119","doi-asserted-by":"publisher","first-page":"6848","DOI":"10.1109\/CVPR.2018.00922","volume":"2018","author":"X Zhang","year":"2018","unstructured":"Zhang X, Zhou X, Lin M, Sun J (2018) Shufflenet: an extremely efficient convolutional neural network for mobile devices. In Proc IEEE Conf Comput Vis Pattern Recognit 2018:6848\u20136856. https:\/\/doi.org\/10.1109\/CVPR.2018.00922","journal-title":"In Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"17192_CR120","doi-asserted-by":"publisher","unstructured":"Ma N, Zhang X, Zheng HT, Sun J (2018) Shufflenet v2: practical guidelines for efficient cnn architecture design. In Proceedings of the European conference on computer vision (ECCV) 11218: 122\u2013138. https:\/\/doi.org\/10.1007\/978-3-030-01264-9_8","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"17192_CR121","doi-asserted-by":"publisher","first-page":"6717","DOI":"10.1109\/ICCV.2019.00682","volume":"2019","author":"Z Qin","year":"2019","unstructured":"Qin Z, Li Z, Zhang Z, Bao Y, Yu G, Peng Y, Sun J (2019) Thundernet: towards real-time generic object detection on mobile devices. In Proceedings of the IEEE\/CVF International Conference on Computer Vision 2019:6717\u20136726. https:\/\/doi.org\/10.1109\/ICCV.2019.00682","journal-title":"In Proceedings of the IEEE\/CVF International Conference on Computer Vision"},{"key":"17192_CR122","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1007\/978-3-030-01249-6_34","volume":"11214","author":"S Mehta","year":"2018","unstructured":"Mehta S, Rastegari M, Caspi A, Shapiro L, Hajishirzi H (2018) Espnet: efficient spatial pyramid of dilated convolutions for semantic segmentation. In Proceedings of the european conference on computer vision (ECCV) 11214:561\u2013580. https:\/\/doi.org\/10.1007\/978-3-030-01249-6_34","journal-title":"In Proceedings of the european conference on computer vision (ECCV)"},{"key":"17192_CR123","first-page":"1608","volume":"31","author":"Y Wang","year":"2018","unstructured":"Wang Y, Xu C, Xu C, Xu C, Tao D (2018) Learning versatile filters for efficient convolutional neural networks. In Advances in Neural Information Processing Systems 31:1608\u20131618","journal-title":"In Advances in Neural Information Processing Systems"},{"key":"17192_CR124","doi-asserted-by":"crossref","unstructured":"Han K, Wang Y, Tian Q, Guo J, Xu C, Xu C (2020) Ghostnet: More features from cheap operations. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 1580\u20131589","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"17192_CR125","doi-asserted-by":"publisher","unstructured":"Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv preprint arXiv:1602.07360.  https:\/\/doi.org\/10.48550\/arXiv.1602.07360","DOI":"10.48550\/arXiv.1602.07360"},{"key":"17192_CR126","doi-asserted-by":"publisher","unstructured":"Gholami A, Kwon K, Wu B, Tai Z, Yue X, Jin P, Keutzer K (2018). Squeezenext: hardware-aware neural network design. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp 1638\u20131647. https:\/\/doi.org\/10.1109\/CVPRW.2018.00215","DOI":"10.1109\/CVPRW.2018.00215"},{"key":"17192_CR127","doi-asserted-by":"publisher","unstructured":"Wu B, Iandola F, Jin PH, Keutzer K (2017) Squeezedet: unified, small, low power fully convolutional neural networks for real-time object detection for autonomous driving. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops,  pp 446\u2013454. https:\/\/doi.org\/10.1109\/CVPRW.2017.60","DOI":"10.1109\/CVPRW.2017.60"},{"issue":"8","key":"17192_CR128","doi-asserted-by":"publisher","first-page":"2570","DOI":"10.1109\/TPAMI.2020.2975796","volume":"43","author":"H Gao","year":"2018","unstructured":"Gao H, Wang Z, Ji S (2018) Channelnets: compact and efficient convolutional neural networks via channel-wise convolutions. In Advances in Neural Information Processing Systems 43(8):2570\u20132581. https:\/\/doi.org\/10.1109\/TPAMI.2020.2975796","journal-title":"In Advances in Neural Information Processing Systems"},{"issue":"8","key":"17192_CR129","doi-asserted-by":"publisher","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","volume":"42","author":"J Hu","year":"2018","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In Proc IEEE Conf Comput Vis Pattern Recognit 42(8):2011\u20132023. https:\/\/doi.org\/10.1109\/TPAMI.2019.2913372","journal-title":"In Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"17192_CR130","doi-asserted-by":"publisher","first-page":"10435","DOI":"10.1109\/CVPR46437.2021.01030","volume":"2021","author":"C Yu","year":"2021","unstructured":"Yu C, Xiao B, Gao C, Yuan L, Zhang L, Sang N, Wang J (2021) Lite-hrnet: a lightweight high-resolution network. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2021:10435\u201310445. https:\/\/doi.org\/10.1109\/CVPR46437.2021.01030","journal-title":"In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"issue":"10","key":"17192_CR131","doi-asserted-by":"publisher","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","volume":"43","author":"J Wang","year":"2020","unstructured":"Wang J, Sun K, Cheng T, Jiang B, Deng C, Zhao Y,... Xiao B, (2020) Deep high-resolution representation learning for visual recognition. IEEE Trans Pattern Anal Mach Intell 43(10):3349\u20133364. https:\/\/doi.org\/10.1109\/TPAMI.2020.2983686","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"10","key":"17192_CR132","doi-asserted-by":"publisher","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","volume":"43","author":"RJ Wang","year":"2018","unstructured":"Wang RJ, Li X, Ling CX (2018) Pelee: a real-time object detection system on mobile devices. In Adv Neural Inf Proces Syst 43(10):3349\u20133364. https:\/\/doi.org\/10.1109\/TPAMI.2020.2983686","journal-title":"In Adv Neural Inf Proces Syst"},{"key":"17192_CR133","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1007\/978-3-030-58580-8_40","volume":"12348","author":"D Zhou","year":"2020","unstructured":"Zhou D, Hou Q, Chen Y, Feng J, Yan S (2020) Rethinking bottleneck structure for efficient mobile network design. European Conference on Computer Vision 12348:680\u2013697. https:\/\/doi.org\/10.1007\/978-3-030-58580-8_40","journal-title":"European Conference on Computer Vision"},{"issue":"1","key":"17192_CR134","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s11036-020-01723-z","volume":"26","author":"Q Zhou","year":"2021","unstructured":"Zhou Q, Wang J, Liu J, Li S, Ou W, Jin X (2021) RSANet: towards real-time object detection with residual semantic-guided attention feature pyramid network. Mobile Networks and Applications 26(1):77\u201387","journal-title":"Mobile Networks and Applications"},{"key":"17192_CR135","volume-title":"Learning Versatile Convolution Filters for Efficient Visual Recognition","author":"K Han","year":"2021","unstructured":"Han K, Wang Y, Xu C, Xu C, Wu E, Tao D (2021) Learning versatile convolution filters for efficient visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"17192_CR136","doi-asserted-by":"publisher","unstructured":"Howard A, Sandler M, Chu G, Chen LC, Chen B, Tan M,... Adam H (2019). Searching for mobilenetv3. In Proceedings of the IEEE\/CVF International Conference on Computer Vision 2019:1314\u20131324. https:\/\/doi.org\/10.1109\/ICCV.2019.00140","DOI":"10.1109\/ICCV.2019.00140"},{"key":"17192_CR137","doi-asserted-by":"publisher","unstructured":"Tan M, Chen B, Pang R, Vasudevan V, Sandler M, Howard A, Le QV (2019) Mnasnet: platform-aware neural architecture search for mobile. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition  2018:2815\u20132823. https:\/\/doi.org\/10.1109\/CVPR.2019.00293","DOI":"10.1109\/CVPR.2019.00293"},{"key":"17192_CR138","doi-asserted-by":"publisher","first-page":"9127","DOI":"10.1109\/CVPR.2018.00951","volume":"2017","author":"B Wu","year":"2018","unstructured":"Wu B, Wan A, Yue X, Jin P, Zhao S, Golmant N,... Keutzer K, (2018) Shift: A zero flop, zero parameter alternative to spatial convolutions. In Proc IEEE Conf Comput Vis Pattern Recognit 2017:9127\u20139135. https:\/\/doi.org\/10.1109\/CVPR.2018.00951","journal-title":"In Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"17192_CR139","doi-asserted-by":"publisher","unstructured":"Yan Z, Li X, Li M, Zuo W, Shan S (2018) Shift-net: image inpainting via deep feature rearrangement. In Proceedings of the European conference on computer vision (ECCV) 11218:3\u201319. https:\/\/doi.org\/10.1007\/978-3-030-01264-9_1","DOI":"10.1007\/978-3-030-01264-9_1"},{"key":"17192_CR140","doi-asserted-by":"publisher","unstructured":"Chen W, Xie D, Zhang Y, Pu S (2019) All you need is a few shifts: designing efficient convolutional neural networks for image classification. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2019:7234\u20137243. https:\/\/doi.org\/10.1109\/CVPR.2019.00741","DOI":"10.1109\/CVPR.2019.00741"},{"key":"17192_CR141","doi-asserted-by":"publisher","unstructured":"Chen H, Wang Y, Xu C, Shi B, Xu C, Tian Q, Xu C (2020) AdderNet: do we really need multiplications in deep learning? In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2019:1465\u20131474. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00154","DOI":"10.1109\/CVPR42600.2020.00154"},{"key":"17192_CR142","doi-asserted-by":"publisher","unstructured":"Elhoushi M, Chen Z, Shafiq F, Tian YH, Li JY (2019) Deepshift: towards multiplication-less neural networks. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition,  pp 2359\u20132368. https:\/\/doi.org\/10.1109\/CVPRW53098.2021.00268","DOI":"10.1109\/CVPRW53098.2021.00268"},{"key":"17192_CR143","unstructured":"You H, Chen X, Zhang Y, Li C, Li S, Liu Z,... Lin Y (2020) Shiftaddnet: a hardware-inspired deep network. In Proceedings of the 34th International Conference on Neural Information Processing Systems (NIPS'20) 233:2771\u20132783."},{"key":"17192_CR144","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1109\/CVPR.2016.91","volume":"2015","author":"J Redmon","year":"2016","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In Proc IEEE Conf Comput Vis Pattern Recognit 2015:779\u2013788. https:\/\/doi.org\/10.1109\/CVPR.2016.91","journal-title":"In Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"17192_CR145","doi-asserted-by":"publisher","first-page":"6517","DOI":"10.1109\/CVPR.2017.690","volume":"2016","author":"J Redmon","year":"2017","unstructured":"Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger. In Proc IEEE Conf Comput Vis Pattern Recognit 2016:6517\u20136525. https:\/\/doi.org\/10.1109\/CVPR.2017.690","journal-title":"In Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"17192_CR146","doi-asserted-by":"publisher","unstructured":"Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767. https:\/\/doi.org\/10.48550\/arXiv.1804.02767","DOI":"10.48550\/arXiv.1804.02767"},{"key":"17192_CR147","doi-asserted-by":"publisher","unstructured":"Bochkovskiy A, Wang CY, Liao HYM (2020) Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934. https:\/\/doi.org\/10.48550\/arXiv.2004.10934","DOI":"10.48550\/arXiv.2004.10934"},{"key":"17192_CR148","doi-asserted-by":"publisher","unstructured":"Huang R, Pedoeem J, Chen C (2018) YOLO-LITE: a real-time object detection algorithm optimized for non-GPU computers. In 2018 IEEE International Conference on Big Data (Big Data), pp 2503\u20132510. https:\/\/doi.org\/10.1109\/BigData.2018.8621865","DOI":"10.1109\/BigData.2018.8621865"},{"key":"17192_CR149","doi-asserted-by":"publisher","unstructured":"Wong A, Famuori M, Shafiee MJ, Li F, Chwyl B, Chung J (2019) Yolo nano: a highly compact you only look once convolutional neural network for object detection.  2019 Fifth Workshop on Energy Efficient Machine Learning and Cognitive Computing - NeurIPS Edition (EMC2-NIPS), pp 22\u201325. https:\/\/doi.org\/10.1109\/EMC2-NIPS53020.2019.00013","DOI":"10.1109\/EMC2-NIPS53020.2019.00013"},{"key":"17192_CR150","doi-asserted-by":"publisher","unstructured":"Barry D, Shah M, Keijsers M, Khan H, Hopman B (2019). XYOLO: a model for real-time object detection in humanoid soccer on low-end hardware. In 2019 International Conference on Image and Vision Computing New Zealand (IVCNZ), pp 1\u20136. https:\/\/doi.org\/10.1109\/IVCNZ48456.2019.8960963","DOI":"10.1109\/IVCNZ48456.2019.8960963"},{"key":"17192_CR151","unstructured":"Qiuqiu D (2020) Yolo-fastest: yolo universal object detection model combined with efficientnet-lite. https:\/\/github.com\/dog-qiuqiu\/Yolo-Fastest"},{"key":"17192_CR152","doi-asserted-by":"publisher","unstructured":"Wang CY, Bochkovskiy A, Liao HYM (2020) Scaled-YOLOv4: Scaling cross stage partial network. In 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021:13024\u201313033. https:\/\/doi.org\/10.1109\/CVPR46437.2021.01283","DOI":"10.1109\/CVPR46437.2021.01283"},{"issue":"2","key":"17192_CR153","doi-asserted-by":"publisher","first-page":"955","DOI":"10.1609\/aaai.v35i2.16179","volume":"35","author":"Y Cai","year":"2021","unstructured":"Cai Y, Li H, Yuan G, Niu W, Li Y, Tang X et al (2021) YOLObile: real-time object detection on mobile devices via compression-compilation co-design. In Proceedings of the AAAI Conference on Artificial Intelligence 35(2):955\u2013963. https:\/\/doi.org\/10.1609\/aaai.v35i2.16179","journal-title":"In Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"17192_CR154","doi-asserted-by":"publisher","unstructured":"Biswas, Debojyoti et al. (2022) Improving the energy efficiency of real-time DNN object detection via compression, transfer learning, and scale prediction. 2022 IEEE International Conference on Networking, Architecture and Storage (NAS), pp. 1\u20138. https:\/\/doi.org\/10.1109\/NAS55553.2022.9925528","DOI":"10.1109\/NAS55553.2022.9925528"},{"key":"17192_CR155","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1007\/978-3-030-01261-8_25","volume":"11217","author":"X Wang","year":"2018","unstructured":"Wang X, Yu F, Dou ZY, Darrell T, Gonzalez JE (2018) Skipnet: learning dynamic routing in convolutional networks. In Proceedings of the European Conference on Computer Vision (ECCV) 11217:420\u2013436. https:\/\/doi.org\/10.1007\/978-3-030-01261-8_25","journal-title":"In Proceedings of the European Conference on Computer Vision (ECCV)"},{"key":"17192_CR156","unstructured":"Tan M, Le Q (2019) Efficientnet: rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning 97:6105\u20136114"},{"key":"17192_CR157","doi-asserted-by":"publisher","unstructured":"Tan M, Pang R, Le QV (2020) Efficientdet: scalable and efficient object detection. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition 2020:10781\u201310790. https:\/\/doi.org\/10.1109\/CVPR42600.2020.01079","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"17192_CR158","doi-asserted-by":"publisher","first-page":"8301","DOI":"10.1109\/CVPR52688.2022.00813","volume":"2022","author":"K Choi","year":"2022","unstructured":"Choi K, Lee H, Hong D, Yu J, Park N, Kim Y, Lee J (2022) It\u2019s all in the teacher: zero-shot quantization brought closer to the teacher. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2022:8301\u20138311. https:\/\/doi.org\/10.1109\/CVPR52688.2022.00813","journal-title":"In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"17192_CR159","first-page":"12329","volume":"2022","author":"Y Zhong","year":"2021","unstructured":"Zhong Y, Lin M, Nan G, Liu J, Zhang B, Tian Y, Ji R (2021) IntraQ: learning synthetic images with intra-class heterogeneity for zero-shot network quantization. IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022:12329\u201312338","journal-title":"IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"17192_CR160","doi-asserted-by":"publisher","unstructured":"Baker B, Gupta O, Naik N, Raskar R (2016) Designing neural network architectures using reinforcement learning. In Proceedings of International Conference on Learning Representations 2017. https:\/\/doi.org\/10.48550\/arXiv.1611.02167","DOI":"10.48550\/arXiv.1611.02167"},{"key":"17192_CR161","first-page":"8697","volume":"2018","author":"B Zoph","year":"2017","unstructured":"Zoph B, Vasudevan V, Shlens J, Le Q (2017) Learning transferable architectures for scalable image recognition. IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2018:8697\u20138710","journal-title":"IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"17192_CR162","first-page":"2423","volume":"2018","author":"Z Zhong","year":"2018","unstructured":"Zhong Z, Yan J, Wu W, Shao J, Liu CL (2018) Practical block-wise neural network architecture generation. In Proc IEEE Conf Comput Vis Pattern Recognit 2018:2423\u20132432","journal-title":"In Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"17192_CR163","first-page":"678","volume":"80","author":"H Cai","year":"2018","unstructured":"Cai H, Yang J, Zhang W, Han S, Yu Y (2018) Path-level network transformation for efficient architecture search. In International Conference on Machine Learning 80:678\u2013687","journal-title":"In International Conference on Machine Learning"},{"issue":"1","key":"17192_CR164","first-page":"2787","volume":"32","author":"H Cai","year":"2018","unstructured":"Cai H, Chen T, Zhang W, Yu Y, Wang J (2018) Efficient architecture search by network transformation. In Proceedings of the AAAI Conference on Artificial Intelligence 32(1):2787\u2013794","journal-title":"In Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"17192_CR165","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1007\/978-3-030-01234-2_48","volume":"11211","author":"Y He","year":"2018","unstructured":"He Y, Lin J, Liu Z, Wang H, Li LJ, Han S (2018) Amc: automl for model compression and acceleration on mobile devices. In Proceedings of the European Conference on Computer Vision (ECCV) 11211:815\u2013832. https:\/\/doi.org\/10.1007\/978-3-030-01234-2_48","journal-title":"In Proceedings of the European Conference on Computer Vision (ECCV)"},{"key":"17192_CR166","first-page":"8366","volume":"31","author":"C Wong","year":"2018","unstructured":"Wong C, Houlsby N, Lu Y, Gesmundo A (2018) Transfer learning with neural automl. In Adv Neural Inf Proces Syst 31:8366\u20138375","journal-title":"In Adv Neural Inf Proces Syst"},{"key":"17192_CR167","first-page":"3296","volume":"2019","author":"Z Liu","year":"2019","unstructured":"Liu Z, Mu H, Zhang X, Guo Z, Yang X, Cheng KT, Sun J (2019) Metapruning: meta learning for automatic neural network channel pruning. In Proceedings of the IEEE\/CVF International Conference on Computer Vision 2019:3296\u20133305","journal-title":"In Proceedings of the IEEE\/CVF International Conference on Computer Vision"},{"key":"17192_CR168","first-page":"2902","volume":"70","author":"E Real","year":"2017","unstructured":"Real E, Moore S, Selle A, Saxena S, Suematsu YL, Tan J et al (2017) Large-scale evolution of image classifiers. In International Conference on Machine Learning 70:2902\u20132911","journal-title":"In International Conference on Machine Learning"},{"issue":"01","key":"17192_CR169","doi-asserted-by":"crossref","first-page":"4780","DOI":"10.1609\/aaai.v33i01.33014780","volume":"33","author":"E Real","year":"2019","unstructured":"Real E, Aggarwal A, Huang Y, Le QV (2019) Regularized evolution for image classifier architecture search. In Proceedings of the AAAI conference on artificial intelligence 33(01):4780\u20134789","journal-title":"In Proceedings of the AAAI conference on artificial intelligence"},{"key":"17192_CR170","doi-asserted-by":"publisher","unstructured":"Liu H, Simonyan K, Vinyals O, Fernando C, Kavukcuoglu K (2017) Hierarchical representations for efficient architecture search. arXiv preprint arXiv:1711.00436, 2017.  https:\/\/doi.org\/10.48550\/arXiv.1711.00436","DOI":"10.48550\/arXiv.1711.00436"},{"key":"17192_CR171","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1007\/978-3-030-58517-4_32","volume":"12361","author":"Z Guo","year":"2020","unstructured":"Guo Z, Zhang X, Mu H, Heng W, Liu Z, Wei Y, Sun J (2020) Single path one-shot neural architecture search with uniform sampling. In European Conference on Computer Vision 12361:544\u2013560. https:\/\/doi.org\/10.1007\/978-3-030-58517-4_32","journal-title":"In European Conference on Computer Vision"},{"key":"17192_CR172","first-page":"3492","volume":"2019","author":"T Veniat","year":"2018","unstructured":"Veniat T, Denoyer L (2018) Learning time\/memory-efficient deep architectures with budgeted super networks. In Proc IEEE Conf Comput Vis Pattern Recognit 2019:3492\u20133500","journal-title":"In Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"17192_CR173","first-page":"10734","volume":"2020","author":"B Wu","year":"2019","unstructured":"Wu B, Dai X, Zhang P, Wang Y, Sun F, Wu Y, Keutzer K (2019) Fbnet: hardware-aware efficient convnet design via differentiable neural architecture search. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2020:10734\u201310742","journal-title":"In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"17192_CR174","first-page":"12965","volume":"2020","author":"A Wan","year":"2020","unstructured":"Wan A, Dai X, Zhang P, He Z, Tian Y, Xie S, Gonzalez JE (2020) Fbnetv2: differentiable neural architecture search for spatial and channel dimensions. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2020:12965\u201312974","journal-title":"In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"17192_CR175","first-page":"16271","volume":"2021","author":"X Dai","year":"2020","unstructured":"Dai X, Wan A, Zhang P, Wu B, He Z, Wei Z, Gonzalez JE (2020) FBNetV3: joint architecture-recipe search using neural acquisition function. 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021:16271\u201316280","journal-title":"2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"17192_CR176","doi-asserted-by":"publisher","unstructured":"Liu H, Simonyan K, Yang Y (2018) Darts: differentiable architecture search.arXiv preprint arXiv:1806.09055. https:\/\/doi.org\/10.48550\/arXiv.1806.09055","DOI":"10.48550\/arXiv.1806.09055"},{"key":"17192_CR177","doi-asserted-by":"publisher","unstructured":"Liu C, Zoph B, Neumann M, Shlens J, Hua W, Li LJ, et al (2018) Progressive neural architecture search. In Proceedings of the European conference on computer vision (ECCV)  11205:19\u201335.  https:\/\/doi.org\/10.1007\/978-3-030-01246-5_2","DOI":"10.1007\/978-3-030-01246-5_2"},{"key":"17192_CR178","doi-asserted-by":"publisher","first-page":"1586","DOI":"10.1109\/CVPR.2018.00171","volume":"2018","author":"A Gordon","year":"2018","unstructured":"Gordon A, Eban E, Nachum O, Chen B, Wu H, Yang TJ, Choi E (2018) Morphnet: fast & simple resource-constrained structure learning of deep networks. In Proc IEEE Conf Comput Vis Pattern Recognit 2018:1586\u20131595. https:\/\/doi.org\/10.1109\/CVPR.2018.00171","journal-title":"In Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"17192_CR179","first-page":"4095","volume":"80","author":"H Pham","year":"2018","unstructured":"Pham H, Guan M, Zoph B, Le Q, Dean J (2018) Efficient neural architecture search via parameters sharing. In International Conference on Machine Learning 80:4095\u20134104","journal-title":"In International Conference on Machine Learning"},{"key":"17192_CR180","doi-asserted-by":"publisher","unstructured":"Xiong Y, Liu H, Gupta S, Akin B, Bender G, Wang Y, et al (2021). Mobiledets: searching for object detection architectures for mobile accelerators. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2021:3825\u20133834.  https:\/\/doi.org\/10.1109\/CVPR46437.2021.00382","DOI":"10.1109\/CVPR46437.2021.00382"},{"key":"17192_CR181","doi-asserted-by":"publisher","unstructured":"Cai H, Zhu L, Han S (2018) Proxylessnas: direct neural architecture search on target task and hardware. In Proceedings of International Conference on Learning Representations 2019. https:\/\/doi.org\/10.48550\/arXiv.1812.00332","DOI":"10.48550\/arXiv.1812.00332"},{"key":"17192_CR182","doi-asserted-by":"publisher","unstructured":"Yu J, Yang L, Xu N, Yang J, Huang T (2018) Slimmable neural networks. In Proceedings of International Conference on Learning Representations 2019. https:\/\/doi.org\/10.48550\/arXiv.1812.08928","DOI":"10.48550\/arXiv.1812.08928"},{"key":"17192_CR183","doi-asserted-by":"publisher","unstructured":"Elsken T, Metzen JH, Hutter F (2017) Simple and efficient architecture search for convolutional neural networks. In Proceedings of International Conference on Learning Representations 2018.  https:\/\/doi.org\/10.48550\/arXiv.1711.04528","DOI":"10.48550\/arXiv.1711.04528"},{"key":"17192_CR184","doi-asserted-by":"publisher","unstructured":"Chen B, Li P, Li B, Lin C, Li C, Sun M, et al (2021). Bn-nas: neural architecture search with batch normalization. In Proceedings of the IEEE\/CVF International Conference on Computer Vision 2021:307\u2013316. https:\/\/doi.org\/10.1109\/ICCV48922.2021.00037","DOI":"10.1109\/ICCV48922.2021.00037"},{"key":"17192_CR185","doi-asserted-by":"publisher","unstructured":"Chen M, Peng H, Fu J, Ling H (2021). Autoformer: searching transformers for visual recognition. In Proceedings of the IEEE\/CVF International Conference on Computer Vision 2021:12270\u201312280. https:\/\/doi.org\/10.1109\/ICCV48922.2021.01205","DOI":"10.1109\/ICCV48922.2021.01205"},{"key":"17192_CR186","volume-title":"Network pruning via transformable architecture search","author":"X Dong","year":"2019","unstructured":"Dong X, Yang Y (2019) Network pruning via transformable architecture search. In Advances in Neural Information Processing Systems"},{"key":"17192_CR187","doi-asserted-by":"publisher","unstructured":"Li X, Zhou Y, Pan Z, Feng J (2019) Partial order pruning: for best speed\/accuracy trade-off in neural architecture search. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2019:9145\u20139153. https:\/\/doi.org\/10.1109\/CVPR.2019.00936","DOI":"10.1109\/CVPR.2019.00936"},{"key":"17192_CR188","doi-asserted-by":"publisher","unstructured":"Cai H, Gan C, Wang T, Zhang Z, Han S (2019) Once-for-all: train one network and specialize it for efficient deployment. In Proceedings of International Conference on Learning Representations 2020. https:\/\/doi.org\/10.48550\/arXiv.1908.09791","DOI":"10.48550\/arXiv.1908.09791"},{"key":"17192_CR189","doi-asserted-by":"publisher","unstructured":"Wang T, Wang K, Cai H, Lin J, Liu Z, Wang H, ... Han S (2020). Apq: joint search for network architecture, pruning and quantization policy. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2020:2075\u20132084. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00215","DOI":"10.1109\/CVPR42600.2020.00215"},{"key":"17192_CR190","first-page":"11711","volume":"33","author":"J Lin","year":"2020","unstructured":"Lin J, Chen WM, Lin Y, Cohn J, Gan C, Han S (2020) Mcunet: tiny deep learning on iot devices. In Adv Neural Inf Proces Syst 33:11711\u201311722","journal-title":"In Adv Neural Inf Proces Syst"},{"issue":"2","key":"17192_CR191","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1007\/s11263-020-01379-y","volume":"129","author":"H Chen","year":"2021","unstructured":"Chen H, Zhang B, Zheng X, Liu J, Ji R, Doermann D, Guo G (2021) Binarized neural architecture search for efficient object recognition. Int J Comput Vision 129(2):501\u2013516","journal-title":"Int J Comput Vision"},{"key":"17192_CR192","doi-asserted-by":"publisher","unstructured":"Zhuo LA, Zhang B, Chen H, Yang L, Chen C, Zhu Y, Doermann D (2020) CP-NAS: child-parent neural architecture search for binary neural networks. In IJCAI 144:1033\u20131039. https:\/\/doi.org\/10.24963\/ijcai.2020\/144","DOI":"10.24963\/ijcai.2020\/144"},{"key":"17192_CR193","doi-asserted-by":"publisher","first-page":"15175","DOI":"10.1109\/CVPR46437.2021.01493","volume":"2021","author":"B Yan","year":"2021","unstructured":"Yan B, Peng H, Wu K, Wang D, Fu J, Lu H (2021) LightTrack: finding lightweight neural networks for object tracking via one-shot architecture search. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2021:15175\u201315184. https:\/\/doi.org\/10.1109\/CVPR46437.2021.01493","journal-title":"In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"17192_CR194","first-page":"3123","volume":"28","author":"M Courbariaux","year":"2015","unstructured":"Courbariaux M, Bengio Y, David JP (2015) Binaryconnect: training deep neural networks with binary weights during propagations. In Adv Neural Inf Proces Syst 28:3123\u20133131","journal-title":"In Adv Neural Inf Proces Syst"},{"key":"17192_CR195","unstructured":"Hubara I, Courbariaux M, Soudry D, El-Yaniv R, Bengio Y (2016) Binarized neural networks. In Proceedings of the 30th International Conference on Neural Information Processing Systems 29:4114\u20134122"},{"key":"17192_CR196","doi-asserted-by":"publisher","unstructured":"Rastegari M, Ordonez V, Redmon J, Farhadi A (2016) Xnor-net: imagenet classification using binary convolutional neural networks. In European conference on computer vision  9908:525\u2013542. https:\/\/doi.org\/10.1007\/978-3-319-46493-0_32","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"17192_CR197","unstructured":"Lin X, Zhao C, Pan W (2017). Towards accurate binary convolutional neural network. In Proceedings of the 31st International Conference on Neural Information Processing Systems 30:344\u2013352"},{"key":"17192_CR198","doi-asserted-by":"publisher","unstructured":"Hou L, Yao Q, Kwok JT (2016) Loss-aware binarization of deep networks. In Proceedings of International Conference on Learning Representations 2017. https:\/\/doi.org\/10.48550\/arXiv.1611.01600","DOI":"10.48550\/arXiv.1611.01600"},{"key":"17192_CR199","doi-asserted-by":"publisher","unstructured":"Liu Z, Wu B, Luo W, Yang X, Liu W, Cheng KT (2018) Bi-real net: enhancing the performance of 1-bit cnns with improved representational capability and advanced training algorithm. In Proceedings of the European Conference on Computer Vision (ECCV) 11219:722\u2013737. https:\/\/doi.org\/10.1007\/978-3-030-01267-0_44","DOI":"10.1007\/978-3-030-01267-0_44"},{"issue":"1","key":"17192_CR200","first-page":"3247","volume":"32","author":"Q Hu","year":"2018","unstructured":"Hu Q, Wang P, Cheng J (2018) From hashing to cnns: training binary weight networks via hashing. In Proceedings of the AAAI Conference on Artificial Intelligence 32(1):3247\u20133254","journal-title":"In Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"17192_CR201","doi-asserted-by":"publisher","unstructured":"Diffenderfer J, Kailkhura B (2020) Multi-prize lottery ticket hypothesis: finding accurate binary neural networks by pruning a randomly weighted network. In International Conference on Learning Representations 2021.arXiv preprint arXiv: 2103.09377. https:\/\/doi.org\/10.48550\/arXiv.2103.09377","DOI":"10.48550\/arXiv.2103.09377"},{"key":"17192_CR202","doi-asserted-by":"publisher","unstructured":"Umuroglu Y, Fraser NJ, Gambardella G, Blott M, Leong P, Jahre M, Vissers K (2017) Finn: a framework for fast, scalable binarized neural network inference. In Proceedings of the 2017 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, pp 65-74. https:\/\/doi.org\/10.1145\/3020078.3021744","DOI":"10.1145\/3020078.3021744"},{"key":"17192_CR203","doi-asserted-by":"publisher","unstructured":"Zhu S, Dong X, Su H (2019) Binary ensemble neural network: more bits per network or more networks per bit? In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2019:4918-4927. https:\/\/doi.org\/10.1109\/CVPR.2019.00506","DOI":"10.1109\/CVPR.2019.00506"},{"key":"17192_CR204","doi-asserted-by":"publisher","unstructured":"Wang Z, Lu J, Tao C, Zhou J, Tian Q (2019) Learning channel-wise interactions for binary convolutional neural networks. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 43(10):3432-3445. https:\/\/doi.org\/10.1109\/TPAMI.2020.2988262","DOI":"10.1109\/TPAMI.2020.2988262"},{"key":"17192_CR205","doi-asserted-by":"publisher","unstructured":"Liu C, Ding W, Xia X, Zhang B, Gu J, Liu J,... Doermann D (2019). Circulant binary convolutional networks: enhancing the performance of 1-bit dcnns with circulant back propagation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2019:2691\u20132699. https:\/\/doi.org\/10.1109\/CVPR.2019.00280","DOI":"10.1109\/CVPR.2019.00280"},{"key":"17192_CR206","doi-asserted-by":"publisher","unstructured":"Ding R, Chin TW, Liu Z, Marculescu D (2019) Regularizing activation distribution for training binarized deep networks. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2019:11408\u201311417. https:\/\/doi.org\/10.1109\/CVPR.2019.01167","DOI":"10.1109\/CVPR.2019.01167"},{"key":"17192_CR207","doi-asserted-by":"publisher","unstructured":"Zhu C, Han S, Mao H, Dally WJ (2016) Trained ternary quantization.  arXiv preprint arXiv:1612.01064. https:\/\/doi.org\/10.48550\/arXiv.1612.01064","DOI":"10.48550\/arXiv.1612.01064"},{"key":"17192_CR208","unstructured":"Li F, Zhang B, Liu B (2016) Ternary weight networks. arXiv preprint arXiv:1605.04711. https:\/\/arxiv.org\/abs\/1605.04711"},{"issue":"10","key":"17192_CR209","first-page":"8538","volume":"35","author":"Y Li","year":"2021","unstructured":"Li Y, Ding W, Liu C, Zhang B, Guo G (2021) TRQ: ternary neural networks with residual quantization. Proc AAAI Conf Artif Intell 35(10):8538\u20138546","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"17192_CR210","first-page":"4820","volume":"2016","author":"J Wu","year":"2016","unstructured":"Wu J, Leng C, Wang Y, Hu Q, Cheng J (2016) Quantized convolutional neural networks for mobile devices. In Proc IEEE Conf Comput Vis Pattern Recognit 2016:4820\u20134828","journal-title":"In Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"17192_CR211","first-page":"5363","volume":"80","author":"J Wu","year":"2018","unstructured":"Wu J, Wang Y, Wu Z, Wang Z, Veeraraghavan A, Lin Y (2018) Deep k-means: re-training and parameter sharing with harder cluster assignments for compressing deep convolutions. In International Conference on Machine Learning 80:5363\u20135372","journal-title":"In International Conference on Machine Learning"},{"key":"17192_CR212","doi-asserted-by":"crossref","unstructured":"Xu Y, Wang Y, Zhou A, Lin W, Xiong H (2018) Deep neural network compression with single and multiple level quantization. In Proceedings of the AAAI Conference on Artificial Intelligence 32(1):335\u2013442","DOI":"10.1609\/aaai.v32i1.11663"},{"key":"17192_CR213","unstructured":"Zhou S, Wu Y, Ni Z, Zhou X, Wen H, Zou Y (2016) Dorefa-net: training low bitwidth convolutional neural networks with low bitwidth gradients. arXiv preprint arXiv:1606.06160. https:\/\/arxiv.org\/abs\/1606.06160"},{"key":"17192_CR214","first-page":"2704","volume":"2018","author":"B Jacob","year":"2018","unstructured":"Jacob B, Kligys S, Chen B, Zhu M, Tang M, Howard A,... Kalenichenko D, (2018) Quantization and training of neural networks for efficient integer-arithmetic-only inference. In Proc IEEE Conf Comput Vis Pattern Recognit 2018:2704\u20132713","journal-title":"In Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"17192_CR215","doi-asserted-by":"crossref","unstructured":"Zhan D, Yang J, Ye D, Hua G (2018) Lq-nets: Learned quantization for highly accurate and compact deep neural networks. In Proceedings of the European conference on computer vision (ECCV). 2018:365\u2013382","DOI":"10.1007\/978-3-030-01237-3_23"},{"key":"17192_CR216","first-page":"4300","volume":"2018","author":"J Faraone","year":"2018","unstructured":"Faraone J, Fraser N, Blott M, Leong PH (2018) Syq: Learning symmetric quantization for efficient deep neural networks. In Proc IEEE Conf Comput Vis Pattern Recognit 2018:4300\u20134309","journal-title":"In Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"17192_CR217","first-page":"4376","volume":"2018","author":"P Wang","year":"2018","unstructured":"Wang P, Hu Q, Zhang Y, Zhang C, Liu Y, Cheng J (2018) Two-step quantization for low-bit neural networks. In Proc IEEE Conf Comput Vis Pattern Recognit 2018:4376\u20134384","journal-title":"In Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"17192_CR218","first-page":"2250","volume":"2020","author":"H Qin","year":"2020","unstructured":"Qin H, Gong R, Liu X, Shen M, Wei Z, Yu F, Song J (2020) Forward and backward information retention for accurate binary neural networks. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2020:2250\u20132259","journal-title":"In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition."},{"key":"17192_CR219","doi-asserted-by":"crossref","unstructured":"Leng C, Dou Z, Li H, Zhu S, Jin R (2018) Extremely low bit neural network: Squeeze the last bit out with admm.  In Proceedings of the AAAI Conference on Artificial Intelligence 32(1):466\u20133473","DOI":"10.1609\/aaai.v32i1.11713"},{"issue":"1","key":"17192_CR220","first-page":"6869","volume":"18","author":"I Hubara","year":"2017","unstructured":"Hubara I, Courbariaux M, Soudry D, El-Yaniv R, Bengio Y (2017) Quantized neural networks: training neural networks with low precision weights and activations. J Machine Learning Res 18(1):6869\u20136898","journal-title":"J Machine Learning Res"},{"issue":"10","key":"17192_CR221","first-page":"8697","volume":"35","author":"X Liu","year":"2021","unstructured":"Liu X, Ye M, Zhou D, Liu Q (2021) Post-training quantization with multiple points: mixed precision without mixed precision. Proc AAAI Conf Artif Intell 35(10):8697\u20138705","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"17192_CR222","doi-asserted-by":"crossref","unstructured":"Kim D, Lee J, Ham B (2021) Distance-aware quantization. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 2021:5271\u20135280","DOI":"10.1109\/ICCV48922.2021.00522"},{"key":"17192_CR223","doi-asserted-by":"crossref","unstructured":"Wang K, Liu Z, Lin Y, Lin J, Han S (2019) Haq: Hardware-aware automated quantization with mixed precision. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2019:8612\u20138620","DOI":"10.1109\/CVPR.2019.00881"},{"key":"17192_CR224","doi-asserted-by":"crossref","unstructured":"Yang H, Gui S, Zhu Y, Liu J (2020) Automatic neural network compression by sparsity-quantization joint learning: A constrained optimization-based approach. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2020:2178\u20132188","DOI":"10.1109\/CVPR42600.2020.00225"},{"key":"17192_CR225","doi-asserted-by":"crossref","unstructured":"Bulat A, Martinez B, Tzimiropoulos G (2020) Bats: binary architecture search. In European Conference on Computer Vision 12368:309\u2013325","DOI":"10.1007\/978-3-030-58592-1_19"},{"key":"17192_CR226","doi-asserted-by":"crossref","unstructured":"Kim D, Singh KP, Choi J (2020) Learning architectures for binary networks. In European conference on computer vision. 12357:575\u2013591","DOI":"10.1007\/978-3-030-58610-2_34"},{"issue":"8","key":"17192_CR227","first-page":"6794","volume":"35","author":"Y Boo","year":"2021","unstructured":"Boo Y, Shin S, Choi J, Sung W (2021) Stochastic precision ensemble: self-knowledge distillation for quantized deep neural networks. Proc AAAI Conf Artif Intell 35(8):6794\u20136802","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"17192_CR228","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42484-020-00025-7","volume":"3","author":"C Zhao","year":"2021","unstructured":"Zhao C, Gao X (2021) QDNN: deep neural networks with quantum layers. Quantum Machine Intell 3:1\u20139","journal-title":"Quantum Machine Intell"},{"key":"17192_CR229","unstructured":"Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. In: Proc Adv Neural Inf Processing Syst Workshop 27:2014"},{"key":"17192_CR230","doi-asserted-by":"crossref","unstructured":"Zhang Y, Xiang T, Hospedales TM, Lu H (2018) Deep mutual learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018:4320\u20134328","DOI":"10.1109\/CVPR.2018.00454"},{"key":"17192_CR231","unstructured":"Kim J, Park S, Kwak N (2018) Paraphrasing complex network: network compression via factor transfer. In Advances in Neural Information Processing Systems. 2018:2765\u20132774"},{"key":"17192_CR232","doi-asserted-by":"crossref","unstructured":"Zhao B, Cui Q, Song R, Qiu Y, Liang J (2022) Decoupled knowledge distillation. In Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition. 2022:11953\u201311962","DOI":"10.1109\/CVPR52688.2022.01165"},{"key":"17192_CR233","doi-asserted-by":"publisher","unstructured":"Romero A, Ballas N, Kahou SE, Chassang A, Gatta C, Bengio Y (2014) Fitnets: hints for thin deep nets. In Proceedings of International Conference on Learning Representations 2015. https:\/\/doi.org\/10.48550\/arXiv.1412.6550","DOI":"10.48550\/arXiv.1412.6550"},{"key":"17192_CR234","volume-title":"Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer","author":"N Komodakis","year":"2017","unstructured":"Komodakis N, Zagoruyko S (2017) Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer. In ICLR"},{"key":"17192_CR235","doi-asserted-by":"publisher","unstructured":"Huang Z, Wang N (2018) Like what you like: knowledge distill via neuron selectivity transfer. In Proceedings of International Conference on Learning Representations 2019.  https:\/\/doi.org\/10.48550\/arXiv.1707.01219","DOI":"10.48550\/arXiv.1707.01219"},{"key":"17192_CR236","doi-asserted-by":"crossref","unstructured":"Tung F, Mori G (2019) Similarity-preserving knowledge distillation. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 2019:1365\u20131374","DOI":"10.1109\/ICCV.2019.00145"},{"key":"17192_CR237","unstructured":"Yang J, Martinez B, Bulat A, Tzimiropoulos G (2020) Knowledge distillation via adaptive instance normalization. arXiv preprint arXiv:2003.04289. https:\/\/arxiv.org\/abs\/2003.04289"},{"key":"17192_CR238","doi-asserted-by":"crossref","unstructured":"Yim J, Joo D, Bae J, Kim J (2017) A gift from knowledge distillation: fast optimization, network minimization and transfer learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017:4133\u20134141.","DOI":"10.1109\/CVPR.2017.754"},{"key":"17192_CR239","doi-asserted-by":"crossref","unstructured":"You S, Xu C, Xu C, Tao D (2017) Learning from multiple teacher networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017:1285\u20131294","DOI":"10.1145\/3097983.3098135"},{"key":"17192_CR240","doi-asserted-by":"publisher","unstructured":"Anil R, Pereyra G, Passos A, Ormandi R, Dahl GE, Hinton GE (2018). Large scale distributed neural network training through online distillation. In Proceedings of International Conference on Learning Representations 2018. https:\/\/doi.org\/10.48550\/arXiv.1804.03235","DOI":"10.48550\/arXiv.1804.03235"},{"key":"17192_CR241","doi-asserted-by":"crossref","unstructured":"Peng B, Jin X, Liu J, Li D, Wu Y, Liu Y, ... Zhang Z (2019) Correlation congruence for knowledge distillation. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 2019:5007\u20135016","DOI":"10.1109\/ICCV.2019.00511"},{"key":"17192_CR242","doi-asserted-by":"crossref","unstructured":"Park W, Kim D, Lu Y, Cho M (2019) Relational knowledge distillation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2019:3967\u20133976","DOI":"10.1109\/CVPR.2019.00409"},{"key":"17192_CR243","doi-asserted-by":"crossref","unstructured":"Liu Y, Cao J, Li B, Yuan C, Hu W, Li Y, Duan Y (2019) Knowledge distillation via instance relationship graph. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2019:7096\u20137104","DOI":"10.1109\/CVPR.2019.00726"},{"key":"17192_CR244","unstructured":"Tian Y, Krishnan D, Isola P (2019) Contrastive representation distillation. In Proceedings of International Conference on Learning Representations, 2020. https:\/\/arxiv.org\/abs\/1910.10699"},{"key":"17192_CR245","doi-asserted-by":"crossref","unstructured":"Zhou S, Wang Y, Chen D, Chen J, Wang X, Wang C, Bu J (2021) Distilling holistic knowledge with graph neural networks. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 2021:10387\u201310396","DOI":"10.1109\/ICCV48922.2021.01022"},{"key":"17192_CR246","doi-asserted-by":"publisher","first-page":"2827","DOI":"10.1109\/CVPR.2016.309","volume":"2016","author":"S Gupta","year":"2016","unstructured":"Gupta S, Hoffman J, Malik J (2016) Cross modal distillation for supervision transfer. In Proc IEEE Conf Comput Vis Pattern Recognit 2016:2827\u20132836. https:\/\/doi.org\/10.1109\/CVPR.2016.309","journal-title":"In Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"17192_CR247","doi-asserted-by":"crossref","unstructured":"Li Q, Jin S, Yan J (2017) Mimicking very efficient network for object detection. In Proceedings of the ieee conference on computer vision and pattern recognition. 2017:6356\u20136364","DOI":"10.1109\/CVPR.2017.776"},{"key":"17192_CR248","first-page":"1276","volume":"2020","author":"Jong-gook Ko","year":"2020","unstructured":"Ko Jong-gook, Yoo Wonyoung (2020) Knowledge distillation based compact model learning method for object detection. International Conference on Information and Communication Technology Convergence (ICTC) 2020:1276\u20131278","journal-title":"International Conference on Information and Communication Technology Convergence (ICTC)"},{"key":"17192_CR249","unstructured":"Chen G, Choi W, Yu X, Han T, Chandraker M (2017) Learning efficient object detection models with knowledge distillation. In Proceedings of the 31st International Conference on Neural Information Processing Systems 2020(30):742\u201351"},{"key":"17192_CR250","doi-asserted-by":"crossref","unstructured":"Wang T, Yuan L, Zhang X, Feng J (2019) Distilling object detectors with fine-grained feature imitation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2019:4933\u20134942","DOI":"10.1109\/CVPR.2019.00507"},{"key":"17192_CR251","doi-asserted-by":"crossref","unstructured":"Chen H, Guo T, Xu C, Li W, Xu C, Xu C, Wang Y (2021) Learning student networks in the wild. 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2021:6424\u20136433","DOI":"10.1109\/CVPR46437.2021.00636"},{"issue":"04","key":"17192_CR252","first-page":"5191","volume":"34","author":"SI Mirzadeh","year":"2020","unstructured":"Mirzadeh SI, Farajtabar M, Li A, Levine N, Matsukawa A, Ghasemzadeh H (2020) Improved knowledge distillation via teacher assistant. Proc AAAI Conf Artif Intell 34(04):5191\u20135198","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"17192_CR253","unstructured":"Gao M, Shen Y, Li Q, Loy CC (2020) Residual Knowledge Distillation. arXiv preprint arXiv: 2002.09168. https:\/\/arxiv.org\/abs\/2002.09168"},{"key":"17192_CR254","unstructured":"Denil M, Shakibi B, Dinh L, Ranzato M, de Freitas N (2013) Predicting parameters in deep learning. Adv Neural Inf Processing Syst. Lake Tahoe 26:2148\u20132156"},{"key":"17192_CR255","doi-asserted-by":"publisher","unstructured":"Lebedev V, Ganin Y, Rakhuba M, Oseledets I, Lempitsky V (2015). Speeding-up convolutional neural networks using fine-tuned cp-decomposition. In International Conference on Learning Representations. https:\/\/doi.org\/10.48550\/arXiv.1412.6553","DOI":"10.48550\/arXiv.1412.6553"},{"key":"17192_CR256","unstructured":"Denton EL, Zaremba W, Bruna J, LeCun Y, Fergus R (2014) Exploiting linear structure within convolutional networks for efficient evaluation. Neural information processing systems foundation (NIPS). 2014: 1269\u20131277"},{"key":"17192_CR257","doi-asserted-by":"crossref","unstructured":"Jaderberg M, Vedaldi A, Zisserman (2014) Speeding up Convolutional Neural Networks with Low Rank Expansions. In British Machine Vision Conference. 2014: 1\u201313","DOI":"10.5244\/C.28.88"},{"key":"17192_CR258","doi-asserted-by":"crossref","unstructured":"Kim Y, Park E, Yoo S, Choi T, Yang L, Shin D (2015) Compression of deep convolutional neural networks for fast and low power mobile applications. CoRR. https:\/\/arxiv.org\/abs\/1511.06530","DOI":"10.14257\/astl.2016.140.36"},{"issue":"10","key":"17192_CR259","doi-asserted-by":"crossref","first-page":"1943","DOI":"10.1109\/TPAMI.2015.2502579","volume":"38","author":"X Zhang","year":"2015","unstructured":"Zhang X, Zou J, He K, Sun J (2015) Accelerating very deep convolutional networks for classification and detection. IEEE Trans Pattern Anal Mach Intell 38(10):1943\u20131955","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"17192_CR260","unstructured":"Novikov A, Podoprikhin D, Osokin A, Vetrov D (2015) Tensorizing neural networks. In Advances in Neural Information Processing Systems. 28:442\u2013450"},{"key":"17192_CR261","unstructured":"Liu B, Wang M, Foroosh H, Tappen MF, Pensky M (2015) Sparse convolutional neural networks. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2015:806\u2013814"},{"key":"17192_CR262","unstructured":"Tai C, Xiao T, Zhang Y, Wang X (2015) Convolutional neural networks with low-rank regularization. arXiv preprint arXiv:1511.06067. https:\/\/arxiv.org\/abs\/1511.06067"},{"key":"17192_CR263","doi-asserted-by":"publisher","first-page":"7370","DOI":"10.1109\/CVPR.2017.15","volume":"2017","author":"X Yu","year":"2017","unstructured":"Yu X, Liu T, Wang X, Tao D (2017) On compressing deep models by low rank and sparse decomposition. Proc IEEE Conf Comput Vis Pattern Recognit 2017:7370\u20137379. https:\/\/doi.org\/10.1109\/CVPR.2017.15","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"17192_CR264","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1007\/978-3-030-01237-3_19","volume":"11212","author":"B Peng","year":"2018","unstructured":"Peng B, Tan W, Li Z, Zhang S, Xie D, Pu S (2018) Extreme network compression via filter group approximation. In Proceedings of the European Conference on Computer Vision (ECCV) 11212:300\u2013316. https:\/\/doi.org\/10.1007\/978-3-030-01237-3_19","journal-title":"In Proceedings of the European Conference on Computer Vision (ECCV)"},{"key":"17192_CR265","doi-asserted-by":"publisher","unstructured":"Ye J, Wang L, Li G, Chen D, Zhe S, Chu X, Xu Z (2018) Learning compact recurrent neural networks with block-term tensor decomposition. In Proc IEEE Conf Comput Vis Pattern Recognit 9378\u20139387. https:\/\/doi.org\/10.1109\/10.1109\/CVPR.2018.00977","DOI":"10.1109\/10.1109\/CVPR.2018.00977"},{"key":"17192_CR266","doi-asserted-by":"publisher","unstructured":"Wang W, Sun Y, Eriksson B, Wang W, Aggarwal V (2018) Wide compression: tensor ring nets. In Proc IEEE Conf Comput Vis Pattern Recognit 9329\u20139338. https:\/\/doi.org\/10.1109\/CVPR.2018.00972","DOI":"10.1109\/CVPR.2018.00972"},{"key":"17192_CR267","doi-asserted-by":"publisher","first-page":"7822","DOI":"10.1109\/CVPR.2019.00801","volume":"2019","author":"J Kossaifi","year":"2019","unstructured":"Kossaifi J, Bulat A, Tzimiropoulos G, Pantic M (2019) T-net: parametrizing fully convolutional nets with a single high-order tensor. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2019:7822\u20137831. https:\/\/doi.org\/10.1109\/CVPR.2019.00801","journal-title":"In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"17192_CR268","doi-asserted-by":"publisher","unstructured":"Idelbayev Y, Carreira-Perpin\u00e1n MA (2020) Low-rank compression of neural nets: learning the rank of each layer. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2020:8046\u20138056. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00807","DOI":"10.1109\/CVPR42600.2020.00807"},{"issue":"01","key":"17192_CR269","first-page":"4287","volume":"33","author":"S Liao","year":"2019","unstructured":"Liao S, Yuan B (2019) CircConv: a structured Convolution with low complexity. In Proc AAAI Conf Artif Intell 33(01):4287\u20134294","journal-title":"In Proc AAAI Conf Artif Intell"},{"key":"17192_CR270","doi-asserted-by":"publisher","first-page":"8015","DOI":"10.1109\/CVPR42600.2020.00804","volume":"2020","author":"Y Li","year":"2020","unstructured":"Li Y, Gu S, Mayer C, Gool LV, Timofte R (2020) Group sparsity: the hinge between filter pruning and decomposition for network compression. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2020:8015\u20138024. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00804","journal-title":"In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"17192_CR271","doi-asserted-by":"publisher","unstructured":"Swaminathan S, Garg D, Kannan R, Andres F (2020) Sparse low rank factorization for deep neural network compression. Neurocomputing 398:185\u2013196. https:\/\/doi.org\/10.1016\/j.neucom.2020.02.035","DOI":"10.1016\/j.neucom.2020.02.035"},{"key":"17192_CR272","unstructured":"Hawkins C, Yang H, Li M, Lai L, Chandra V (2021) Low-rank+sparse tensor compression for neural networks. arXiv preprint arXiv:2111.01697.  https:\/\/doi.org\/10.48550\/arXiv.2111.01697"},{"key":"17192_CR273","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016). Ssd: single shot multibox detector. In Computer Vision \u2013 ECCV 2016 9905:21\u201337.  https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2","DOI":"10.1007\/978-3-319-46448-0_2"},{"issue":"6","key":"17192_CR274","first-page":"84","volume":"60","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In Adv Neural Inf Proces Syst 60(6):84\u201390","journal-title":"In Adv Neural Inf Proces Syst"},{"issue":"1","key":"17192_CR275","first-page":"4278","volume":"31","author":"C Szegedy","year":"2017","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. Proc AAAI Conf Artif Intell 31(1):4278\u20134284","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"17192_CR276","doi-asserted-by":"publisher","unstructured":"Simonyan K, Zisserman A (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.  https:\/\/doi.org\/10.48550\/arXiv.1409.1556","DOI":"10.48550\/arXiv.1409.1556"},{"key":"17192_CR277","doi-asserted-by":"publisher","unstructured":"Zhang T, Qi G, Xiao B, Wang J (2017) Interleaved Group Convolutions for Deep Neural Networks.arXiv preprint arXiv:1707.02725. https:\/\/doi.org\/10.48550\/arXiv.1707.02725","DOI":"10.48550\/arXiv.1707.02725"},{"key":"17192_CR278","doi-asserted-by":"publisher","unstructured":"Zhang Q, Li J, Yao M, Song L, Zhou H, Li Z, Meng W, Zhang X, Wang G (2019) VarGNet: variable group convolutional neural network for efficient embedded computing. arXiv preprint arXiv:1907.05653. https:\/\/doi.org\/10.48550\/arXiv.1907.05653","DOI":"10.48550\/arXiv.1907.05653"},{"key":"17192_CR279","first-page":"578","volume":"2022","author":"K Yang","year":"2022","unstructured":"Yang K, Jiao Z, Liang J, Lei H, Li C, Zhong Z (2022) An application case of object detection model based on Yolov3-SPP model pruning. IEEE Int Conf Artif Intell Comput App (ICAICA) 2022:578\u2013582","journal-title":"IEEE Int Conf Artif Intell Comput App (ICAICA)"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17192-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-17192-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17192-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T11:34:33Z","timestamp":1715081673000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-17192-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,2]]},"references-count":279,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["17192"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-17192-x","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,2]]},"assertion":[{"value":"18 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 September 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 November 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}