{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:10:47Z","timestamp":1775578247463,"version":"3.50.1"},"reference-count":64,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"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":["2018AAA0102205"],"award-info":[{"award-number":["2018AAA0102205"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61902399"],"award-info":[{"award-number":["61902399"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61721004"],"award-info":[{"award-number":["61721004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1836220"],"award-info":[{"award-number":["U1836220"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1705262"],"award-info":[{"award-number":["U1705262"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61832002"],"award-info":[{"award-number":["61832002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61720106006"],"award-info":[{"award-number":["61720106006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U20B2070"],"award-info":[{"award-number":["U20B2070"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976199"],"award-info":[{"award-number":["61976199"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["L201001"],"award-info":[{"award-number":["L201001"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002367","name":"Key Research Program of Frontier Sciences, Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["QYZDJSSW-JSC039"],"award-info":[{"award-number":["QYZDJSSW-JSC039"]}],"id":[{"id":"10.13039\/501100002367","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Circuits Syst. Video Technol."],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1109\/tcsvt.2021.3081729","type":"journal-article","created":{"date-parts":[[2021,5,19]],"date-time":"2021-05-19T21:43:35Z","timestamp":1621460615000},"page":"2057-2067","source":"Crossref","is-referenced-by-count":27,"title":["Margin-Based Adversarial Joint Alignment Domain Adaptation"],"prefix":"10.1109","volume":"32","author":[{"given":"Yukun","family":"Zuo","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, University of Science and Technology of China, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8125-2864","authenticated-orcid":false,"given":"Hantao","family":"Yao","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4345-856X","authenticated-orcid":false,"given":"Liansheng","family":"Zhuang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, University of Science and Technology of China, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8343-9665","authenticated-orcid":false,"given":"Changsheng","family":"Xu","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"},{"key":"ref2","first-page":"1180","article-title":"Unsupervised domain adaptation by backpropagation","volume-title":"Proc. ICML","author":"Ganin"},{"key":"ref3","first-page":"7523","article-title":"On learning invariant representations for domain adaptation","volume-title":"Proc. ICML","author":"Zhao"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICME46284.2020.9102756"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093579"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00948"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3065254"},{"issue":"1","key":"ref8","first-page":"1","article-title":"Adaptation based on generalized discrepancy","volume":"20","author":"Cortes","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"ref9","first-page":"2208","article-title":"Deep transfer learning with joint adaptation networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Long"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10306"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.5555\/2946645.2946704"},{"key":"ref12","first-page":"5419","article-title":"Learning semantic representations for unsupervised domain adaptation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xie"},{"key":"ref13","article-title":"Bridging theory and algorithm for domain adaptation","author":"Zhang","year":"2019","journal-title":"arXiv:1904.05801"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_36"},{"key":"ref15","first-page":"343","article-title":"Domain separation networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Bousmalis"},{"key":"ref16","first-page":"1","article-title":"Self-ensembling for visual domain adaptation","volume-title":"Proc. ICLR","author":"French"},{"key":"ref17","article-title":"VisDA: The visual domain adaptation challenge","author":"Peng","year":"2017","journal-title":"arXiv:1710.06924"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3240508.3240512"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00503"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2018.2842206"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2020.2968484"},{"key":"ref23","first-page":"97","article-title":"Learning transferable features with deep adaptation networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Long"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"ref25","article-title":"Central moment discrepancy (CMD) for domain-invariant representation learning","author":"Zellinger","year":"2017","journal-title":"arXiv:1702.08811"},{"key":"ref26","first-page":"136","article-title":"Unsupervised domain adaptation with residual transfer networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Long"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00392"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.3156\/jsoft.29.5_177_2"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2019.2963318"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58526-6_32"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2016.7899860"},{"key":"ref32","first-page":"9345","article-title":"Co-regularized alignment for unsupervised domain adaptation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Kumar"},{"key":"ref33","first-page":"1081","article-title":"Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation","volume-title":"Proc. ICML","volume":"97","author":"Chen"},{"key":"ref34","first-page":"1","article-title":"Cycada: Cycle-consistent adversarial domain adaptation","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Hoffman"},{"key":"ref35","first-page":"2234","article-title":"Improved techniques for training gans","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Salimans"},{"key":"ref36","first-page":"6510","article-title":"Good semi-supervised learning that requires a bad gan","volume-title":"Proc. NIPS","author":"Dai"},{"key":"ref37","first-page":"10440","article-title":"Margingan: Adversarial training in semi-supervised learning","volume-title":"Proc. NIPS","author":"Dong"},{"key":"ref38","article-title":"Temporal ensembling for semi-supervised learning","author":"Laine","year":"2016","journal-title":"arXiv:1610.02242"},{"key":"ref39","first-page":"01780","article-title":"Weight-averaged consistency targets improve semi-supervised deep learning results","volume":"1703","author":"Tarvainen","year":"2018","journal-title":"CoRR"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2760512"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2783902"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2867198"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.01004"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00887"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref46","first-page":"2988","article-title":"Asymmetric tri-training for unsupervised domain adaptation","volume-title":"Proc. ICML","author":"Saito"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00845"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.301"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01053"},{"key":"ref50","article-title":"MiniMax entropy network: Learning category-invariant features for domain adaptation","author":"Tao","year":"2019","journal-title":"arXiv:1904.09601"},{"key":"ref51","article-title":"Adversarial dropout regularization","author":"Saito","year":"2017","journal-title":"arXiv:1711.01575"},{"key":"ref52","first-page":"1","article-title":"Reading digits in natural images with unsupervised feature learning","volume-title":"Proc. NIPS","author":"Netzer"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref54","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref55","first-page":"215","article-title":"An analysis of single-layer networks in unsupervised feature learning","volume-title":"Proc. AISTATS","author":"Coates"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2011.6033395"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-02895-8_52"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref60","article-title":"Shake-shake regularization","author":"Gastaldi","year":"2017","journal-title":"arXiv:1705.07485"},{"key":"ref61","first-page":"2172","article-title":"Infogan: Interpretable representation learning by information maximizing generative adversarial nets","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Chen"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6247911"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-011-0841-3"}],"container-title":["IEEE Transactions on Circuits and Systems for Video Technology"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/76\/9749157\/09435354.pdf?arnumber=9435354","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T00:22:05Z","timestamp":1704846125000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9435354\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4]]},"references-count":64,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tcsvt.2021.3081729","relation":{},"ISSN":["1051-8215","1558-2205"],"issn-type":[{"value":"1051-8215","type":"print"},{"value":"1558-2205","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4]]}}}