{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T21:06:17Z","timestamp":1768683977325,"version":"3.49.0"},"reference-count":30,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,10,9]],"date-time":"2022-10-09T00:00:00Z","timestamp":1665273600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,10,9]],"date-time":"2022-10-09T00:00:00Z","timestamp":1665273600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,9]]},"DOI":"10.1109\/smc53654.2022.9945569","type":"proceedings-article","created":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T20:49:04Z","timestamp":1668804544000},"page":"1032-1038","source":"Crossref","is-referenced-by-count":2,"title":["A Sensitivity-based Pruning Method for Convolutional Neural Networks"],"prefix":"10.1109","author":[{"given":"Cankun","family":"Zhong","sequence":"first","affiliation":[{"name":"South China University of Technology,School of Computer Science and Engineering,Guangzhou,China,510006"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"He","sequence":"additional","affiliation":[{"name":"South China University of Technology,School of Computer Science and Engineering,Guangzhou,China,510006"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifei","family":"An","sequence":"additional","affiliation":[{"name":"South China University of Technology,School of Computer Science and Engineering,Guangzhou,China,510006"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wing W. Y.","family":"Ng","sequence":"additional","affiliation":[{"name":"South China University of Technology,School of Computer Science and Engineering,Guangzhou,China,510006"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ting","family":"Wang","sequence":"additional","affiliation":[{"name":"South China University of Technology,Guangzhou First People&#x2019;s Hospital, School of Medicine,Department of Radiology,Guangzhou,China,510006"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2020.3032728"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3415048.3416114"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/72.572092"},{"key":"ref12","first-page":"10170","article-title":"Synaptic strength for convolutional neural network","author":"lin","year":"2018","journal-title":"Proc Advances in Neural Information Processing Systems"},{"key":"ref13","article-title":"Reliability evaluation of pruned neural networks against errors on parameters","author":"gao","year":"2020","journal-title":"In IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT)"},{"key":"ref14","first-page":"1","article-title":"Pruning filters for efficient convnets","author":"li","year":"2017","journal-title":"Proc International Conference on Learning Representations"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2906563"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00447"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.541"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00958"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/318"},{"key":"ref28","first-page":"1","article-title":"Slimmable Neural Networks","author":"yu","year":"2019","journal-title":"Proc International Conference on Learning Representations"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/SMC52423.2021.9658976"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/SMC52423.2021.9659289"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/SMC52423.2021.9658855"},{"key":"ref6","first-page":"1387","article-title":"Dynamic network surgery for efficient DNNs","author":"guo","year":"2016","journal-title":"Proc Advances in Neural Information Processing Systems"},{"key":"ref29","first-page":"1","article-title":"Dynamic channel pruning: feature boosting and suppression","author":"gao","year":"2019","journal-title":"Proc International Conference on Learning Representations"},{"key":"ref5","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 International Conference on Learning Representations"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.03.082"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00289"},{"key":"ref2","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"Proc International Conference on Learning Representations"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3084527"},{"key":"ref1","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Advances in Neural Information Processing Systems"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2993932"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9533406"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3052016"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3049470"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.04.021"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00290"},{"key":"ref25","first-page":"1645","article-title":"October. Convolution Acceleration: Query Based Filter Pruning","author":"feeney","year":"2019","journal-title":"2021 IEEE International Conference on Systems Man and Cybernetics (SMC)"}],"event":{"name":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","location":"Prague, Czech Republic","start":{"date-parts":[[2022,10,9]]},"end":{"date-parts":[[2022,10,12]]}},"container-title":["2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9945068\/9945069\/09945569.pdf?arnumber=9945569","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T19:53:39Z","timestamp":1670874819000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9945569\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,9]]},"references-count":30,"URL":"https:\/\/doi.org\/10.1109\/smc53654.2022.9945569","relation":{},"subject":[],"published":{"date-parts":[[2022,10,9]]}}}