{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T18:39:38Z","timestamp":1783967978133,"version":"3.55.0"},"reference-count":29,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61802130"],"award-info":[{"award-number":["61802130"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Guangdong Natural Science Foundation","doi-asserted-by":"publisher","award":["2019A1515012152"],"award-info":[{"award-number":["2019A1515012152"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Guangdong Natural Science Foundation","doi-asserted-by":"publisher","award":["2021A1515012651"],"award-info":[{"award-number":["2021A1515012651"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council Future Fellowship","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100015539","name":"Australian Government","doi-asserted-by":"publisher","award":["FT210100268"],"award-info":[{"award-number":["FT210100268"]}],"id":[{"id":"10.13039\/100015539","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1109\/tnnls.2022.3151138","type":"journal-article","created":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T20:35:39Z","timestamp":1646166939000},"page":"9528-9535","source":"Crossref","is-referenced-by-count":203,"title":["DualConv: Dual Convolutional Kernels for Lightweight Deep Neural Networks"],"prefix":"10.1109","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4120-7510","authenticated-orcid":false,"given":"Jiachen","family":"Zhong","sequence":"first","affiliation":[{"name":"School of Software Engineering, South China University of Technology, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5614-9731","authenticated-orcid":false,"given":"Junying","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Software Engineering, South China University of Technology, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5206-3842","authenticated-orcid":false,"given":"Ajmal","family":"Mian","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The University of Western Australia, Crawley, WA, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Han"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00958"},{"key":"ref3","first-page":"2178","article-title":"Runtime neural pruning","volume-title":"Proc. Annu. Conf. Neural Inf. Process. Syst.","author":"Lin"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.541"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.155"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12262"},{"key":"ref7","first-page":"1","article-title":"Pruning filters for efficient ConvNets","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Li"},{"key":"ref8","first-page":"6778","article-title":"Structured Bayesian pruning via log-normal multiplicative noise","volume-title":"Proc. Annu. Conf. Neural Inf. Process. Syst.","author":"Neklyudov"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"ref10","first-page":"177","article-title":"Comparing biases for minimal network construction with back-propagation","volume-title":"Proc. Annu. Conf. Neural Inf. Process. Syst.","author":"Hanson"},{"key":"ref11","first-page":"598","article-title":"Optimal brain damage","volume-title":"Proc. Annu. Conf. Neural Inf. Process. Syst.","author":"LeCun"},{"key":"ref12","first-page":"164","article-title":"Second order derivatives for network pruning: Optimal brain surgeon","volume-title":"Proc. Annu. Conf. Neural Inf. Process. Syst.","author":"Hassibi"},{"key":"ref13","first-page":"1","article-title":"Improving the speed of neural networks on CPUs","volume-title":"Proc. Deep Learn. Unsupervised Feature Learn. Workshop","author":"Vanhoucke"},{"key":"ref14","article-title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size","volume-title":"arXiv:1602.07360","author":"Iandola","year":"2016"},{"key":"ref15","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","volume-title":"arXiv:1704.04861","author":"Howard","year":"2017"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00497"},{"key":"ref19","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Simonyan"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref21","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref23","article-title":"YOLOv3: An incremental improvement","volume-title":"arXiv:1804.02767","author":"Redmon","year":"2018"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4"},{"key":"ref25","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Annu. Conf. Neural Inf. Process. Syst.","author":"Krizhevsky"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.633"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref29","first-page":"1","article-title":"Striving for simplicity: The all convolutional net","volume-title":"Proc. Int. Conf. Learn. Represent. Workshop","author":"Springenberg"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10299535\/09723436.pdf?arnumber=9723436","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T23:41:29Z","timestamp":1705534889000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9723436\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11]]},"references-count":29,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2022.3151138","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11]]}}}