{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T19:32:26Z","timestamp":1776886346855,"version":"3.51.2"},"reference-count":65,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFB2202703"],"award-info":[{"award-number":["2018YFB2202703"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research Grants Council of Hong Kong, SAR","award":["CUHK14209420"],"award-info":[{"award-number":["CUHK14209420"]}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20201145"],"award-info":[{"award-number":["BK20201145"]}],"id":[{"id":"10.13039\/501100004608","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":[[2022,12]]},"DOI":"10.1109\/tnnls.2021.3084900","type":"journal-article","created":{"date-parts":[[2021,6,10]],"date-time":"2021-06-10T19:44:52Z","timestamp":1623354292000},"page":"7367-7379","source":"Crossref","is-referenced-by-count":22,"title":["An Efficient Sharing Grouped Convolution via Bayesian Learning"],"prefix":"10.1109","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9195-6619","authenticated-orcid":false,"given":"Tinghuan","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong, SAR, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Southeast University, Wuxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5153-6698","authenticated-orcid":false,"given":"Qi","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong, SAR, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2188-8195","authenticated-orcid":false,"given":"Meng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronics Science and Engineering, Southeast University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2075-8583","authenticated-orcid":false,"given":"Guoqing","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronics Science and Engineering, Southeast University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0943-7714","authenticated-orcid":false,"given":"Hao","family":"Geng","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong, SAR, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qianru","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronics Science and Engineering, Southeast University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6406-4810","authenticated-orcid":false,"given":"Bei","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong, SAR, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","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 Int Conf Learn Represent (ICLR)"},{"key":"ref38","first-page":"1","article-title":"DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression","author":"wiedemann","year":"2019","journal-title":"Proc ICML Workshop"},{"key":"ref33","first-page":"10988","article-title":"GroupReduce: Block-wise low-rank approximation for neural language model shrinking","author":"chen","year":"2018","journal-title":"Proc Conf Neural Inf Process Syst (NIPS)"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.11.028"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICTAI.2019.00060"},{"key":"ref30","first-page":"1","article-title":"PCAS: Pruning channels with attention statistics for deep network compression","author":"yamamoto","year":"2019","journal-title":"Proc Brit Mach Vis Conf (BMVC)"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1139"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2020.102898"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01097"},{"key":"ref34","first-page":"742","article-title":"Learning efficient object detection models with knowledge distillation","author":"chen","year":"2017","journal-title":"Proc Conf Neural Inf Process Syst (NIPS)"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2013.2241055"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804441"},{"key":"ref61","author":"press","year":"2007","journal-title":"Numerical Recipes The Art of Scientific Computing"},{"key":"ref63","year":"2019","journal-title":"Group LASSO Solver"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8852463"},{"key":"ref64","first-page":"3371","article-title":"Learning with structured sparsity","volume":"12","author":"huang","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref27","first-page":"1135","article-title":"Learning both weights and connections for efficient neural network","author":"han","year":"2015","journal-title":"Proc Conf Neural Inf Process Syst (NIPS)"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2019.2961233"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00431"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.09.038"},{"key":"ref22","first-page":"2074","article-title":"Learning structured sparsity in deep neural networks","author":"wen","year":"2016","journal-title":"Proc Conf Neural Inf Process Syst (NIPS)"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01237-3_12"},{"key":"ref24","article-title":"A novel channel pruning method for deep neural network compression","author":"hu","year":"2018","journal-title":"arXiv 1805 11394"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.155"},{"key":"ref26","first-page":"1","article-title":"Pruning filters for efficient convnets","author":"li","year":"2017","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.298"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.469"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1145\/3316781.3317909"},{"key":"ref58","article-title":"Leveraging spatial correlation for sensor drift calibration in smart building","author":"chen","year":"2020","journal-title":"IEEE Trans Comput -Aided Design Integr Circuits Syst"},{"key":"ref57","article-title":"Auto-encoding variational Bayes","author":"kingma","year":"2013","journal-title":"arXiv 1312 6114"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2020.2977090"},{"key":"ref55","first-page":"2575","article-title":"Variational dropout and the local reparameterization trick","author":"kingma","year":"2015","journal-title":"Proc Conf Neural Inf Process Syst (NIPS)"},{"key":"ref54","first-page":"3288","article-title":"Bayesian compression for deep learning","author":"louizos","year":"2017","journal-title":"Proc Conf Neural Inf Process Syst (NIPS)"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00245"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58539-6_9"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00364"},{"key":"ref40","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00926"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1080\/09332480.2014.914768"},{"key":"ref14","year":"2019","journal-title":"CIFAR-10 and CIFAR-100 Datasets"},{"key":"ref15","year":"2019","journal-title":"The ImageNet dataset"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00291"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2020.2972520"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3316781.3317875"},{"key":"ref4","first-page":"21","article-title":"SSD: Single shot multibox detector","author":"liu","year":"2016","journal-title":"Proc Eur Conf Comput Vis (ECCV)"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/453"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3400302.3415661"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3289602.3293902"},{"key":"ref7","first-page":"1","article-title":"Energy-driven DNN dataflow optimization on FPGA","author":"sun","year":"2019","journal-title":"Proc IEEE\/ACM Int Conf Comput -Aided Design (ICCAD)"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref9","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Conf Neural Inf Process Syst (NIPS)"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107610"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2020.2992384"},{"key":"ref48","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"howard","year":"2017","journal-title":"arXiv 1704 04861"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00053"},{"key":"ref41","first-page":"1","article-title":"Differentiable learning-to-normalize via switchable normalization","author":"luo","year":"2019","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref44","first-page":"8645","article-title":"Channel equilibrium networks for learning deep representation","author":"shao","year":"2020","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01274"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/9966944\/09451543.pdf?arnumber=9451543","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T19:56:04Z","timestamp":1671479764000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9451543\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12]]},"references-count":65,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2021.3084900","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12]]}}}