{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T08:12:40Z","timestamp":1782202360027,"version":"3.54.5"},"reference-count":38,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China General Program","doi-asserted-by":"publisher","award":["61772317"],"award-info":[{"award-number":["61772317"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China General Program","doi-asserted-by":"publisher","award":["62072284"],"award-info":[{"award-number":["62072284"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009108","name":"\u201cQilu\u201d Young Talent Program of Shandong University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009108","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research Intern Program of Alibaba Group"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Image Process."],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/tip.2022.3175432","type":"journal-article","created":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T19:34:35Z","timestamp":1653075275000},"page":"3726-3736","source":"Crossref","is-referenced-by-count":210,"title":["DO-Conv: Depthwise Over-Parameterized Convolutional Layer"],"prefix":"10.1109","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8614-7366","authenticated-orcid":false,"given":"Jinming","family":"Cao","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Shandong University, Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yangyan","family":"Li","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingchao","family":"Sun","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Chen","sequence":"additional","affiliation":[{"name":"The Rachel and Selim Benin School of Computer Science and Engineering, Hebrew University of Jerusalem, Jerusalem, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dani","family":"Lischinski","sequence":"additional","affiliation":[{"name":"The Rachel and Selim Benin School of Computer Science and Engineering, Hebrew University of Jerusalem, Jerusalem, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6777-7445","authenticated-orcid":false,"given":"Daniel","family":"Cohen-Or","sequence":"additional","affiliation":[{"name":"School of Computer Science, Tel Aviv University, Tel Aviv, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4702-036X","authenticated-orcid":false,"given":"Baoquan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronics Engineering and Computer Science, Peking University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changhe","family":"Tu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Shandong University, Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"244","article-title":"On the optimization of deep networks: Implicit acceleration by overparameterization","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Arora"},{"key":"ref2","first-page":"242","article-title":"A convergence theory for deep learning via over-parameterization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Allen-Zhu"},{"key":"ref3","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Krizhevsky"},{"key":"ref4","article-title":"Very deep convolutional networks for large-scale image recognition","volume-title":"Proc. ICLR","author":"Simonyan"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref7","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017","journal-title":"arXiv:1704.04861"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00953"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00060"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00353"},{"key":"ref13","article-title":"ExpandNets: Linear over-parameterization to train compact convolutional networks","author":"Guo","year":"2018","journal-title":"arXiv:1811.10495"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00200"},{"key":"ref15","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"Ioffe","year":"2015","journal-title":"arXiv:1502.03167"},{"key":"ref16","first-page":"901","article-title":"Weight normalization: A simple reparameterization to accelerate training of deep neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Salimans"},{"key":"ref17","first-page":"2483","article-title":"How does batch normalization help optimization?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Santurkar"},{"key":"ref18","first-page":"7694","article-title":"Understanding batch normalization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Bjorck"},{"key":"ref19","article-title":"Exponential convergence rates for batch normalization: The power of length-direction decoupling in non-convex optimization","author":"Kohler","year":"2018","journal-title":"arXiv:1805.10694"},{"key":"ref20","first-page":"806","article-title":"Exponential convergence rates for batch normalization: The power of length-direction decoupling in non-convex optimization","volume-title":"Proc. 22nd Int. Conf. Artif. Intell. Statist.","author":"Kohler"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref24","volume-title":"TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems","author":"Abadi","year":"2015"},{"key":"ref25","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"issue":"23","key":"ref29","first-page":"1","article-title":"GluonCV and GluonNLP: Deep learning in computer vision and natural language processing","volume":"21","author":"Guo","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-014-0733-5"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref32","article-title":"Rethinking atrous convolution for semantic image segmentation","author":"Chen","year":"2017","journal-title":"arXiv:1706.05587"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref36","first-page":"2951","article-title":"Practical Bayesian optimization of machine learning algorithms","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Snoek"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.5244\/C.30.87"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"}],"container-title":["IEEE Transactions on Image Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/83\/9626658\/09779456.pdf?arnumber=9779456","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T22:09:11Z","timestamp":1705961351000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9779456\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":38,"URL":"https:\/\/doi.org\/10.1109\/tip.2022.3175432","relation":{},"ISSN":["1057-7149","1941-0042"],"issn-type":[{"value":"1057-7149","type":"print"},{"value":"1941-0042","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}