{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T06:24:24Z","timestamp":1770618264625,"version":"3.49.0"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2022,3,8]],"date-time":"2022-03-08T00:00:00Z","timestamp":1646697600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,8]],"date-time":"2022-03-08T00:00:00Z","timestamp":1646697600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1007\/s13042-022-01530-w","type":"journal-article","created":{"date-parts":[[2022,3,8]],"date-time":"2022-03-08T16:20:26Z","timestamp":1646756426000},"page":"2403-2414","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Network pruning via probing the importance of filters"],"prefix":"10.1007","volume":"13","author":[{"given":"Jiandong","family":"Kuang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7323-5896","authenticated-orcid":false,"given":"Mingwen","family":"Shao","sequence":"additional","affiliation":[]},{"given":"Ran","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Wangmeng","family":"Zuo","sequence":"additional","affiliation":[]},{"given":"Weiping","family":"Ding","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,8]]},"reference":[{"key":"1530_CR1","unstructured":"Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2015) Semantic image segmentation with deep convolutional nets and fully connected crfs. In: Int. conf. on learning representations (ICLR)"},{"key":"1530_CR2","doi-asserted-by":"crossref","unstructured":"Chen P, Liu S, Zhao H, Jia J (2021) Distilling knowledge via knowledge review. In: Proc. IEEE conf. computer vision and pattern recognition, pp 5008\u20135017","DOI":"10.1109\/CVPR46437.2021.00497"},{"key":"1530_CR3","doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proc. IEEE conf. computer vision and pattern recognition, pp 1800\u20131807","DOI":"10.1109\/CVPR.2017.195"},{"key":"1530_CR4","unstructured":"Chu X, Zhang B, Xu R, Li J (2019) Fairnas: rethinking evaluation fairness of weight sharing neural architecture search. arXiv preprint arXiv:1907.01845"},{"key":"1530_CR5","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. Adv Neural Inf Process Syst 28:3123\u20133131","journal-title":"Adv Neural Inf Process Syst"},{"key":"1530_CR6","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: Proc. IEEE conf. computer vision and pattern recognition, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"1530_CR7","unstructured":"Ding X, Ding G, Guo Y, Han J, Yan C (2019) Approximated oracle filter pruning for destructive cnn width optimization. In: Int. conf. on machine learning (ICML), pp 1607\u20131616"},{"key":"1530_CR8","first-page":"760","volume":"32","author":"X Dong","year":"2019","unstructured":"Dong X, Yang Y (2019) Network pruning via transformable architecture search. Adv Neural Inf Process Syst 32:760\u2013771","journal-title":"Adv Neural Inf Process Syst"},{"key":"1530_CR9","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proc. IEEE conf. computer vision and pattern recognition, pp 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"key":"1530_CR10","first-page":"1387","volume":"29","author":"Y Guo","year":"2016","unstructured":"Guo Y, Yao A, Chen Y (2016) Dynamic network surgery for efficient dnns. Adv Neural Inf Process Syst 29:1387\u20131395","journal-title":"Adv Neural Inf Process Syst"},{"key":"1530_CR11","doi-asserted-by":"crossref","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: Proc. European conf. computer vision (ECCV), pp 544\u2013560","DOI":"10.1007\/978-3-030-58517-4_32"},{"key":"1530_CR12","first-page":"1135","volume":"28","author":"S Han","year":"2015","unstructured":"Han S, Pool J, Tran J, Dally WJ (2015) Learning both weights and connections for efficient neural networks. Adv Neural Inf Process Syst 28:1135\u20131143","journal-title":"Adv Neural Inf Process Syst"},{"key":"1530_CR13","unstructured":"Han S, Mao H, Dally WJ (2016) Deep compression: compressing deep neural networks with pruning, trained quantization and Huffman coding. In: Int. conf. on learning representations (ICLR)"},{"key":"1530_CR14","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proc. IEEE conf. computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"1530_CR15","doi-asserted-by":"crossref","unstructured":"He Y, Zhang X, Sun J (2017) Channel pruning for accelerating very deep neural networks. In: The IEEE int. conf. computer vision (ICCV), pp 1398\u20131406","DOI":"10.1109\/ICCV.2017.155"},{"key":"1530_CR16","doi-asserted-by":"crossref","unstructured":"He Y, Kang G, Dong X, Fu Y, Yang Y (2018) Soft filter pruning for accelerating deep convolutional neural networks. In: Int. joint conf. artificial intelligence (IJCAI), pp 2234\u20132240","DOI":"10.24963\/ijcai.2018\/309"},{"key":"1530_CR17","doi-asserted-by":"crossref","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: Proc. European conf. computer vision (ECCV), pp 784\u2013800","DOI":"10.1007\/978-3-030-01234-2_48"},{"key":"1530_CR18","doi-asserted-by":"crossref","unstructured":"He Y, Liu P, Wang Z, Hu Z, Yang Y (2019) Filter pruning via geometric median for deep convolutional neural networks acceleration. In: Proc. IEEE conf. computer vision and pattern recognition, pp 4340\u20134349","DOI":"10.1109\/CVPR.2019.00447"},{"key":"1530_CR19","doi-asserted-by":"crossref","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: Proc. IEEE conf. computer vision and pattern recognition, pp \u20132018","DOI":"10.1109\/CVPR42600.2020.00208"},{"key":"1530_CR20","unstructured":"Hinton GE, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531"},{"key":"1530_CR21","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, van der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proc. IEEE conf. computer vision and pattern recognition, pp 2261\u20132269","DOI":"10.1109\/CVPR.2017.243"},{"issue":"1","key":"1530_CR22","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 Mach Learn Res 18(1):6869\u20136898","journal-title":"J Mach Learn Res"},{"key":"1530_CR23","unstructured":"Hu H, Peng R, Tai YW, Tang CK (2016) Network trimming: a data-driven neuron pruning approach towards efficient deep architectures. arXiv preprint arXiv:1607.03250"},{"key":"1530_CR24","doi-asserted-by":"crossref","unstructured":"Jaderberg M, Vedaldi A, Zisserman A (2014) Speeding up convolutional neural networks with low rank expansions. In: British machine vision conference (BMVC)","DOI":"10.5244\/C.28.88"},{"key":"1530_CR25","unstructured":"Krizhevsky A (2009) Learning multiple layers of features from tiny images. In: Technical report"},{"issue":"6","key":"1530_CR26","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390","journal-title":"Commun ACM"},{"key":"1530_CR27","unstructured":"Li H, Kadav A, Durdanovic I, Samet H, Graf HP (2017) Pruning filters for efficient convnets. In: Int. conf. on learning representations (ICLR)"},{"key":"1530_CR28","doi-asserted-by":"crossref","unstructured":"Li B, Wu B, Su J, Wang G, Lin L (2020) Eagleeye: fast sub-net evaluation for efficient neural network pruning. In: Proc. European conf. computer vision (ECCV), pp 639\u2013654","DOI":"10.1007\/978-3-030-58536-5_38"},{"key":"1530_CR29","unstructured":"Li Y, Gong R, Tan X, Yang Y, Hu P, Zhang Q, Yu F, Wang W, Gu S (2021) Brecq: pushing the limit of post-training quantization by block reconstruction. In: Int. conf. on learning representations (ICLR)"},{"key":"1530_CR30","doi-asserted-by":"crossref","unstructured":"Lin S, Ji R, Yan C, Zhang B, Cao L, Ye Q, Huang F, Doermann D (2019) Towards optimal structured cnn pruning via generative adversarial learning. In: Proc. IEEE conf. computer vision and pattern recognition, pp 2790\u20132799","DOI":"10.1109\/CVPR.2019.00290"},{"key":"1530_CR31","doi-asserted-by":"crossref","unstructured":"Lin M, Ji R, Wang Y, Zhang Y, Zhang B, Tian Y, Shao L (2020) Hrank: Filter pruning using high-rank feature map. In: Proc. IEEE conf. computer vision and pattern recognition, pp 1529\u20131538","DOI":"10.1109\/CVPR42600.2020.00160"},{"key":"1530_CR32","doi-asserted-by":"crossref","unstructured":"Liu Z, Li J, Shen Z, Huang G, Yan S, Zhang C (2017) Learning efficient convolutional networks through network slimming. In: The IEEE int. conf. computer vision (ICCV), pp 2755\u20132763","DOI":"10.1109\/ICCV.2017.298"},{"key":"1530_CR33","doi-asserted-by":"crossref","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: The IEEE int. conf. computer vision (ICCV), pp 3296\u20133305","DOI":"10.1109\/ICCV.2019.00339"},{"key":"1530_CR34","unstructured":"Liu H, Simonyan K, Yang Y (2019) Darts: differentiable architecture search. In: Int. conf. on learning representations (ICLR)"},{"key":"1530_CR35","doi-asserted-by":"crossref","unstructured":"Luo JH, Wu J, Lin W (2017) Thinet: a filter level pruning method for deep neural network compression. In: The IEEE int. conf. computer vision (ICCV), pp 5068\u20135076","DOI":"10.1109\/ICCV.2017.541"},{"key":"1530_CR36","unstructured":"Molchanov P, Tyree S, Karras T, Aila T, Kautz J (2017) Pruning convolutional neural networks for resource efficient inference. In: Int. conf. on learning representations (ICLR)"},{"key":"1530_CR37","doi-asserted-by":"crossref","unstructured":"Ning X, Zhao T, Li W, Lei P, Wang Y, Yang H (2020) Dsa: more efficient budgeted pruning via differentiable sparsity allocation. In: Proc. European conf. computer vision (ECCV), pp 592\u2013607","DOI":"10.1007\/978-3-030-58580-8_35"},{"key":"1530_CR38","unstructured":"Petsiuk V, Das A, Saenko K (2018) Rise: randomized input sampling for explanation of black-box models. In: British machine vision conference (BMVC), p 151"},{"issue":"6","key":"1530_CR39","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick R, Sun J (2017) Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137\u20131149","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"1530_CR40","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1007\/s11760-020-01760-x","volume":"15","author":"M Shao","year":"2021","unstructured":"Shao M, Dai J, Kuang J, Meng D (2021) A dynamic CNN pruning method based on matrix similarity. Signal Image Video Process 15(2):381\u2013389. https:\/\/doi.org\/10.1007\/s11760-020-01760-x","journal-title":"Signal Image Video Process"},{"key":"1530_CR41","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-021-01411-8","author":"M Shao","year":"2021","unstructured":"Shao M, Dai J, Wang R, Zuo W (2021) CSHE: network pruning by using cluster similarity and matrix eigenvalues. Int J Mach Learn Cybernet. https:\/\/doi.org\/10.1007\/s13042-021-01411-8","journal-title":"Int J Mach Learn Cybernet"},{"key":"1530_CR42","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Int. conf. on learning representations (ICLR)"},{"key":"1530_CR43","doi-asserted-by":"crossref","unstructured":"Tang Y, Wang Y, Xu Y, Deng Y, Xu C, Tao D, Xu C (2021) Manifold regularized dynamic network pruning. In: Proc. IEEE conf. computer vision and pattern recognition, pp 5018\u20135028","DOI":"10.1109\/CVPR46437.2021.00498"},{"key":"1530_CR44","first-page":"2074","volume":"29","author":"W Wen","year":"2016","unstructured":"Wen W, Wu C, Wang Y, Chen Y, Li H (2016) Learning structured sparsity in deep neural networks. Adv Neural Inf Process Syst 29:2074\u20132082","journal-title":"Adv Neural Inf Process Syst"},{"key":"1530_CR45","doi-asserted-by":"crossref","unstructured":"Yamamoto K (2021) Learnable companding quantization for accurate low-bit neural networks. In: Proc. IEEE conf. computer vision and pattern recognition, pp 5029\u20135038","DOI":"10.1109\/CVPR46437.2021.00499"},{"key":"1530_CR46","unstructured":"Ye J, Lu X, Lin ZL, Wang JZ (2018) Rethinking the smaller-norm-less-informative assumption in channel pruning of convolution layers. In: Int. conf. on learning representations (ICLR)"},{"key":"1530_CR47","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 Process Syst 32:2133\u20132144","journal-title":"Adv Neural Inf Process Syst"},{"key":"1530_CR48","doi-asserted-by":"crossref","unstructured":"Yu J, Huang T (2019) Universally slimmable networks and improved training techniques. In: The ieee int. conf. computer vision (ICCV), pp 1803\u20131811","DOI":"10.1109\/ICCV.2019.00189"},{"key":"1530_CR49","doi-asserted-by":"crossref","unstructured":"Zhao C, Ni B, Zhang J, Zhao Q, Zhang W, Tian Q (2019) Variational convolutional neural network pruning. In: Proc. IEEE conf. computer vision and pattern recognition, pp 2780\u20132789","DOI":"10.1109\/CVPR.2019.00289"},{"key":"1530_CR50","first-page":"883","volume":"31","author":"Z Zhuang","year":"2018","unstructured":"Zhuang Z, Tan M, Zhuang B, Liu J, Guo Y, Wu Q, Huang J, Zhu J (2018) Discrimination-aware channel pruning for deep neural networks. Adv Neural Inf Process Syst 31:883\u2013894","journal-title":"Adv Neural Inf Process Syst"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01530-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-022-01530-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01530-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,29]],"date-time":"2022-07-29T10:50:50Z","timestamp":1659091850000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-022-01530-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,8]]},"references-count":50,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["1530"],"URL":"https:\/\/doi.org\/10.1007\/s13042-022-01530-w","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,8]]},"assertion":[{"value":"15 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 February 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 March 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}