{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:21:24Z","timestamp":1740108084121,"version":"3.37.3"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T00:00:00Z","timestamp":1647907200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T00:00:00Z","timestamp":1647907200000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s00521-022-07127-2","type":"journal-article","created":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T03:03:59Z","timestamp":1647918239000},"page":"12807-12818","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Filter pruning via expectation-maximization"],"prefix":"10.1007","volume":"34","author":[{"given":"Sheng","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanjing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linlin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8476-4371","authenticated-orcid":false,"given":"Baochang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dianmin","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kexin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,22]]},"reference":[{"key":"7127_CR1","unstructured":"Denil M, Shakibi B, Dinh L, Ranzato M, De Freitas N (2013) Predicting parameters in deep learning. In: NIPS, pp 2148\u20132156"},{"key":"7127_CR2","unstructured":"Denton EL, Zaremba W, Bruna J, LeCun Y, Fergus R (2014) Exploiting linear structure within convolutional networks for efficient evaluation. In: NIPS, pp 1269\u20131277"},{"key":"7127_CR3","doi-asserted-by":"crossref","unstructured":"Ding X, Ding G, Guo Y, Han J (2019) Centripetal sgd for pruning very deep convolutional networks with complicated structure. In: CVPR, pp 4943\u20134953","DOI":"10.1109\/CVPR.2019.00508"},{"key":"7127_CR4","doi-asserted-by":"crossref","unstructured":"Dong X, Huang J, Yang Y, Yan S (2017) More is less: A more complicated network with less inference complexity. In: CVPR, pp 5840\u20135848","DOI":"10.1109\/CVPR.2017.205"},{"issue":"2","key":"7127_CR5","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (voc) challenge. Int J Comput Vis 88(2):303\u2013338","journal-title":"Int J Comput Vis"},{"key":"7127_CR6","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: CVPR, pp 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"key":"7127_CR7","doi-asserted-by":"crossref","unstructured":"Gu J, Li C, Zhang B, Han J, Cao X, Liu J, Doermann D (2019) Projection convolutional neural networks for 1-bit cnns via discrete back propagation. In: AAAI, vol 33, pp 8344\u20138351","DOI":"10.1609\/aaai.v33i01.33018344"},{"key":"7127_CR8","unstructured":"Han S, Pool J, Tran J, Dally W (2015) Learning both weights and connections for efficient neural network. In: NIPS, pp 1135\u20131143"},{"key":"7127_CR9","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: CVPR, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"7127_CR10","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. In: ECCV, pp 630\u2013645","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"7127_CR11","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: IJCAI, pp 1\u20138","DOI":"10.24963\/ijcai.2018\/309"},{"key":"7127_CR12","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: CVPR, pp 4340\u20134349","DOI":"10.1109\/CVPR.2019.00447"},{"key":"7127_CR13","doi-asserted-by":"crossref","unstructured":"He Y, Zhang X, Sun J (2017) Channel pruning for accelerating very deep neural networks. In: ICCV, pp 1389\u20131397","DOI":"10.1109\/ICCV.2017.155"},{"key":"7127_CR14","unstructured":"Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531"},{"key":"7127_CR15","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: ICML, pp 448\u2013456"},{"key":"7127_CR16","unstructured":"Krizhevsky A, Hinton G et al (2009) Learning multiple layers of features from tiny images. Technical report, Citeseer"},{"key":"7127_CR17","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: NIPS, pp 1097\u20131105"},{"key":"7127_CR18","unstructured":"LeCun Y, Denker JS, Solla SA (1990) Optimal brain damage. In: NIPS, pp 598\u2013605"},{"key":"7127_CR19","unstructured":"Li H, Kadav A, Durdanovic I, Samet H, Graf HP (2016) Pruning filters for efficient convnets. In: ICLR, pp 1\u201313"},{"key":"7127_CR20","doi-asserted-by":"crossref","unstructured":"Li Y, Lin S, Liu J, Ye Q, Wang M, Chao F, Yang F, Ma J, Tian Q, Ji R (2021) Towards compact cnns via collaborative compression. In: CVPR, pp 6438\u20136447","DOI":"10.1109\/CVPR46437.2021.00637"},{"key":"7127_CR21","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: CVPR, pp 1529\u20131538","DOI":"10.1109\/CVPR42600.2020.00160"},{"key":"7127_CR22","doi-asserted-by":"crossref","unstructured":"Lin M, Ji R, Zhang Y, Zhang B, Wu Y, Tian Y (2020) Channel pruning via automatic structure search. In: IJCAI, pp 1\u20137","DOI":"10.24963\/ijcai.2020\/94"},{"issue":"2","key":"7127_CR23","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1109\/TNNLS.2019.2906563","volume":"31","author":"S Lin","year":"2019","unstructured":"Lin S, Ji R, Li Y, Deng C, Li X (2019) Toward compact convnets via structure-sparsity regularized filter pruning. IEEE Trans Neural Netw Learn Syst 31(2):574\u2013588","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"7127_CR24","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":"7127_CR25","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: CVPR, pp 2790\u20132799","DOI":"10.1109\/CVPR.2019.00290"},{"key":"7127_CR26","doi-asserted-by":"crossref","unstructured":"Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft coco: common objects in context. In: ECCV, pp 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"7127_CR27","doi-asserted-by":"crossref","unstructured":"Liu J, Zhuang B, Zhuang Z, Guo Y, Huang J, Zhu J, Tan M (2021) Discrimination-aware network pruning for deep model compression. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2021.3066410"},{"key":"7127_CR28","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: ECCV, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"7127_CR29","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: ICCV, pp 3296\u20133305","DOI":"10.1109\/ICCV.2019.00339"},{"key":"7127_CR30","doi-asserted-by":"crossref","unstructured":"Luo JH, Wu J, Lin W (2017) Thinet: A filter level pruning method for deep neural network compression. In: ICCV, pp 5058\u20135066","DOI":"10.1109\/ICCV.2017.541"},{"key":"7127_CR31","unstructured":"Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: ICML, pp 807\u2013814"},{"key":"7127_CR32","doi-asserted-by":"crossref","unstructured":"Rastegari M, Ordonez V, Redmon J, Farhadi A (2016) Xnor-net: Imagenet classification using binary convolutional neural networks. In: ECCV, pp 525\u2013542","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"7127_CR33","unstructured":"Romero A, Ballas N, Kahou SE, Chassang A, Gatta C, Bengio Y (2015) Fitnets: Hints for thin deep nets. In: ICLR, pp 1\u201313"},{"issue":"3","key":"7127_CR34","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211\u2013252","journal-title":"Int J Comput Vis"},{"key":"7127_CR35","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: ICLR"},{"key":"7127_CR36","doi-asserted-by":"crossref","unstructured":"Wang X, Zhang B, Li C, Ji R, Han J, Cao X, Liu J (2018) Modulated convolutional networks. In: CVPR, pp 840\u2013848","DOI":"10.1109\/CVPR.2018.00094"},{"key":"7127_CR37","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.neucom.2020.03.056","volume":"403","author":"Z Xie","year":"2020","unstructured":"Xie Z, Zhu L, Zhao L, Tao B, Liu L, Tao W (2020) Localization-aware channel pruning for object detection. Neurocomputing 403:400\u2013408","journal-title":"Neurocomputing"},{"key":"7127_CR38","doi-asserted-by":"crossref","unstructured":"Xu S, Chen H, Gong X, Liu K, L\u00fc J, Zhang B (2021) Efficient structured pruning based on deep feature stabilization. Neural Comput Appl 1\u201312","DOI":"10.1007\/s00521-021-05828-8"},{"key":"7127_CR39","unstructured":"Xu S, Li Y, Zhao J, Zhang B, Guo G (2021) Poem: 1-bit point-wise operations based on expectation-maximization for efficient point cloud processing. In: BMVC"},{"key":"7127_CR40","doi-asserted-by":"crossref","unstructured":"Xu S, Zhao J, Lu J, Zhang B, Han S, Doermann D (2021) Layer-wise searching for 1-bit detectors. In: CVPR, pp 5682\u20135691","DOI":"10.1109\/CVPR46437.2021.00563"},{"key":"7127_CR41","doi-asserted-by":"crossref","unstructured":"Xuan G, Zhang W, Chai P (2001) Em algorithms of gaussian mixture model and hidden markov model. In: Proceedings 2001 international conference on image processing (Cat. No. 01CH37205), vol 1. IEEE, pp 145\u2013148","DOI":"10.1109\/ICIP.2001.958974"},{"key":"7127_CR42","doi-asserted-by":"crossref","unstructured":"Yu R, Li A, Chen CF, Lai JH, Morariu VI, Han X, Gao M, Lin CY, Davis LS (2018) Nisp: pruning networks using neuron importance score propagation. In: CVPR, pp 9194\u20139203","DOI":"10.1109\/CVPR.2018.00958"},{"key":"7127_CR43","doi-asserted-by":"crossref","unstructured":"Zhang X, Zou J, Ming X, He K, Sun J (2015) Efficient and accurate approximations of nonlinear convolutional networks. In: CVPR, pp 1984\u20131992","DOI":"10.1109\/CVPR.2015.7298809"},{"key":"7127_CR44","doi-asserted-by":"crossref","unstructured":"Zhao J, Xu S, Zhang B, Gu J, Doermann D, Guo G (2021) Towards compact 1-bit cnns via bayesian learning. Int J Comput Vis 1\u201325","DOI":"10.1007\/s11263-021-01543-y"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07127-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07127-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07127-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,23]],"date-time":"2022-07-23T10:13:23Z","timestamp":1658571203000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07127-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,22]]},"references-count":44,"journal-issue":{"issue":"15","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["7127"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07127-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2022,3,22]]},"assertion":[{"value":"15 September 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 February 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 March 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}