{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T04:17:25Z","timestamp":1741753045228,"version":"3.38.0"},"reference-count":24,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:00:00Z","timestamp":1730592000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:00:00Z","timestamp":1730592000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100015015","name":"Ningbo University of Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100015015","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,3]]},"DOI":"10.1109\/iecon55916.2024.10905786","type":"proceedings-article","created":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T17:32:07Z","timestamp":1741627927000},"page":"1-6","source":"Crossref","is-referenced-by-count":0,"title":["Pruning CNN based on Combinational Filter Deletion"],"prefix":"10.1109","author":[{"given":"Wenli","family":"Huang","sequence":"first","affiliation":[{"name":"Ningbo University of Technology,School of Electronic and Information Engineering,Ningbo,China"}]},{"given":"Xiujie","family":"Wang","sequence":"additional","affiliation":[{"name":"Science and Technology on Communication Networks Laboratory,Shijiazhuang,China"}]},{"given":"Zhihong","family":"Zhao","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology,Research Institute of Intelligent Control and Systems,Harbin,China"}]},{"given":"Li","family":"Su","sequence":"additional","affiliation":[{"name":"Ningbo University of Technology,School of Electronics and Information Engineering,Ningbo,China"}]},{"given":"Shuai","family":"Sui","sequence":"additional","affiliation":[{"name":"Liaoning University of Technology,Jinzhou,China"}]},{"given":"Jinjun","family":"Wang","sequence":"additional","affiliation":[{"name":"Xi’an Jiaotong University,Institute of Artificial Intelligence and Robotic,Xi’an,China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"article-title":"Very deep convolutional networks for large-scale image recognition","volume-title":"ICLR","author":"Simonyan","key":"ref2"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298809"},{"article-title":"Compressing neural networks with the hashing trick","volume-title":"ICML","author":"Chen","key":"ref4"},{"article-title":"Exploiting linear structure within convolutional networks for efficient evaluation","volume-title":"NIPS","author":"Denton","key":"ref5"},{"article-title":"Pruning filters for efficient convnets","volume-title":"ICLR","author":"Li","key":"ref6"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00289"},{"article-title":"Rethinking the value of network pruning","volume-title":"ICLR","author":"Liu","key":"ref8"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00290"},{"article-title":"Pruning convolutional neural networks for resource efficient inference","volume-title":"ICLR","author":"Molchanov","key":"ref10"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.155"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.541"},{"article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding","volume-title":"ICLR","author":"Han","key":"ref13"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/525"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00160"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00447"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00100"},{"key":"ref18","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009","journal-title":"Citeseer"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"article-title":"Learning both weights and connections for efficient neural networks","volume-title":"NIPS","author":"Han","key":"ref20"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.298"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TCSI.2022.3193444"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58598-3_36"},{"article-title":"Soft threshold weight reparameterization for learnable sparsity","volume-title":"ICML","author":"Kusupati","key":"ref24"}],"event":{"name":"IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society","start":{"date-parts":[[2024,11,3]]},"location":"Chicago, IL, USA","end":{"date-parts":[[2024,11,6]]}},"container-title":["IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10904979\/10905066\/10905786.pdf?arnumber=10905786","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T05:21:57Z","timestamp":1741670517000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10905786\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,3]]},"references-count":24,"URL":"https:\/\/doi.org\/10.1109\/iecon55916.2024.10905786","relation":{},"subject":[],"published":{"date-parts":[[2024,11,3]]}}}