{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T21:07:22Z","timestamp":1768338442313,"version":"3.49.0"},"reference-count":64,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61925107"],"award-info":[{"award-number":["61925107"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1936202"],"award-info":[{"award-number":["U1936202"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Image Process."],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/tip.2020.3035028","type":"journal-article","created":{"date-parts":[[2020,11,13]],"date-time":"2020-11-13T21:29:17Z","timestamp":1605302957000},"page":"293-304","source":"Crossref","is-referenced-by-count":57,"title":["Where to Prune: Using LSTM to Guide Data-Dependent Soft Pruning"],"prefix":"10.1109","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0137-9975","authenticated-orcid":false,"given":"Guiguang","family":"Ding","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0888-4923","authenticated-orcid":false,"given":"Shuo","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Zizhou","family":"Jia","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Zhong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4361-956X","authenticated-orcid":false,"given":"Jungong","family":"Han","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/s12065-007-0002-4"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref33","article-title":"PCAS: Pruning channels with attention statistics for deep network compression","author":"yamamoto","year":"2018","journal-title":"arXiv 1806 05382"},{"key":"ref32","article-title":"N2N learning: Network to network compression via policy gradient reinforcement learning","author":"ashok","year":"2017","journal-title":"arXiv 1709 06030"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00721"},{"key":"ref30","article-title":"Data-driven compression of convolutional neural networks","author":"pahwa","year":"2019","journal-title":"arXiv 1911 12740"},{"key":"ref37","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"howard","year":"2017","journal-title":"arXiv 1704 04861"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref35","article-title":"ShuffleNet: An extremely efficient convolutional neural network for mobile devices","author":"zhang","year":"2017","journal-title":"arXiv 1707 01083"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_48"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00508"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2882155"},{"key":"ref61","first-page":"6382","article-title":"Global sparse momentum SGD for pruning very deep neural networks","author":"ding","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst (NeurIPS)"},{"key":"ref63","first-page":"3277","article-title":"Zero-shot learning with transferred samples","volume":"28","author":"guo","year":"2019","journal-title":"IEEE Trans Image Process"},{"key":"ref28","first-page":"875","article-title":"Discrimination-aware channel pruning for deep neural networks","author":"zhuang","year":"2018","journal-title":"Proc NIPS"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.2967645"},{"key":"ref27","first-page":"10149","article-title":"Synaptic strength for convolutional neural network","author":"lin","year":"2018","journal-title":"Proc NIPS"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/309"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref1","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"Proc ICLR"},{"key":"ref20","article-title":"Network trimming: A data-driven neuron pruning approach towards efficient deep architectures","author":"hu","year":"2016","journal-title":"arXiv 1607 03250"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.298"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.541"},{"key":"ref24","first-page":"1","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding","author":"han","year":"2016","journal-title":"Proc ICLR"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref26","first-page":"2074","article-title":"Learning structured sparsity in deep neural networks","author":"wen","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.02.029"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/445"},{"key":"ref51","first-page":"34","volume":"10","author":"lecun","year":"1998","journal-title":"MNIST handwritten digit database 1998"},{"key":"ref59","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"Proc ICML"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/336"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093331"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00447"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00958"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.155"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref52","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2867198"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1162\/106365602320169811"},{"key":"ref12","article-title":"AlignedReID: Surpassing human-level performance in person re-identification","author":"zhang","year":"2017","journal-title":"arXiv 1711 08184"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"ref15","first-page":"598","article-title":"Optimal brain damage","volume":"2","author":"le cun","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref16","first-page":"164","article-title":"Second order derivatives for network pruning: Optimal brain surgeon","author":"hassibi","year":"1993","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref17","first-page":"1269","article-title":"Exploiting linear structure within convolutional networks for efficient evaluation","author":"denton","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref18","first-page":"1","article-title":"Pruning filters for efficient convnets","author":"li","year":"2017","journal-title":"Proc ICLR"},{"key":"ref19","first-page":"1","article-title":"Pruning convolutional neural networks for resource efficient transfer learning","author":"molchanov","year":"2017","journal-title":"Proc ICLR"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref3","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2855406"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298935"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref7","first-page":"2048","article-title":"Show, attend and tell: Neural image caption generation with visual attention","author":"xu","year":"2015","journal-title":"Proc ICML"},{"key":"ref49","article-title":"Squeeze-and-excitation networks","author":"hu","year":"2017","journal-title":"arXiv 1709 01507"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2865280"},{"key":"ref46","article-title":"Learnable embedding space for efficient neural architecture compression","author":"cao","year":"2019","journal-title":"arXiv 1902 00383"},{"key":"ref45","article-title":"MnasNet: Platform-aware neural architecture search for mobile","author":"tan","year":"2018","journal-title":"arXiv 1807 11626"},{"key":"ref48","first-page":"2654","article-title":"Do deep nets really need to be deep","author":"ba","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/BF00992696"},{"key":"ref42","first-page":"1","article-title":"Neural architecture search with reinforcement learning","author":"zoph","year":"2017","journal-title":"Proc ICLR"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/3071178.3071229"},{"key":"ref44","article-title":"Learning transferable architectures for scalable image recognition","author":"zoph","year":"2017","journal-title":"arXiv 1707 07012"},{"key":"ref43","first-page":"1","article-title":"Designing neural network architectures using reinforcement learning","author":"baker","year":"2016","journal-title":"Proc ICLR"}],"container-title":["IEEE Transactions on Image Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/83\/9263394\/09258919.pdf?arnumber=9258919","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:50:58Z","timestamp":1652194258000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9258919\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":64,"URL":"https:\/\/doi.org\/10.1109\/tip.2020.3035028","relation":{},"ISSN":["1057-7149","1941-0042"],"issn-type":[{"value":"1057-7149","type":"print"},{"value":"1941-0042","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}