{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T15:10:18Z","timestamp":1777129818274,"version":"3.51.4"},"reference-count":66,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"10","license":[{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"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":["61672444"],"award-info":[{"award-number":["61672444"]}],"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":["61988101"],"award-info":[{"award-number":["61988101"]}],"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":["61876162"],"award-info":[{"award-number":["61876162"]}],"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":["62161160338"],"award-info":[{"award-number":["62161160338"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"NSFC\/Research Grants Council (RGC) Joint Research Scheme","doi-asserted-by":"publisher","award":["N_HKBU214\/21"],"award-info":[{"award-number":["N_HKBU214\/21"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"General Research Fund of RGC","award":["12201321"],"award-info":[{"award-number":["12201321"]}]},{"DOI":"10.13039\/501100001747","name":"Hong Kong Baptist University","doi-asserted-by":"publisher","award":["RC-FNRA-IG\/18-19\/SCI\/03"],"award-info":[{"award-number":["RC-FNRA-IG\/18-19\/SCI\/03"]}],"id":[{"id":"10.13039\/501100001747","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001747","name":"Hong Kong Baptist University","doi-asserted-by":"publisher","award":["RC-IRCMs\/18-19\/SCI\/01"],"award-info":[{"award-number":["RC-IRCMs\/18-19\/SCI\/01"]}],"id":[{"id":"10.13039\/501100001747","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Innovation and Technology Fund of Innovation and Technology Commission of the Government, Hong Kong","award":["ITS\/339\/18"],"award-info":[{"award-number":["ITS\/339\/18"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFB1701104"],"award-info":[{"award-number":["2018YFB1701104"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Program of Liaoning Province","award":["2020JH2\/10500001"],"award-info":[{"award-number":["2020JH2\/10500001"]}]},{"name":"Science and Technology Program of Liaoning Province","award":["2020JH1\/10100008"],"award-info":[{"award-number":["2020JH1\/10100008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1109\/tnnls.2022.3145034","type":"journal-article","created":{"date-parts":[[2022,2,7]],"date-time":"2022-02-07T20:51:26Z","timestamp":1644267086000},"page":"7621-7634","source":"Crossref","is-referenced-by-count":30,"title":["Contrastive Learning Assisted-Alignment for Partial Domain Adaptation"],"prefix":"10.1109","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1997-1854","authenticated-orcid":false,"given":"Cuie","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Computer Science, Hong Kong Baptist University, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7629-4648","authenticated-orcid":false,"given":"Yiu-Ming","family":"Cheung","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hong Kong Baptist University, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3735-0672","authenticated-orcid":false,"given":"Jinliang","family":"Ding","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6802-2463","authenticated-orcid":false,"given":"Kay Chen","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Computing, The Hong Kong Polytechnic University, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4865-8026","authenticated-orcid":false,"given":"Bing","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4463-9538","authenticated-orcid":false,"given":"Mengjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref13","first-page":"2030","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"ganin","year":"2015","journal-title":"J Mach Learn Res"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"ref12","first-page":"443","article-title":"Deep coral: Correlation alignment for deep domain adaptation","author":"sun","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref56","first-page":"4171","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","volume":"1","author":"devlin","year":"2019","journal-title":"Proc Conf North Amer Chapter Assoc Comput Linguistics Hum Lang Technol"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00288"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.572"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/MLSP.2018.8517092"},{"key":"ref58","first-page":"2066","article-title":"Geodesic flow kernel for unsupervised domain adaptation","author":"gong","year":"2012","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref53","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","author":"chen","year":"2020","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3360309"},{"key":"ref55","first-page":"18661","article-title":"Supervised contrastive learning","volume":"33","author":"khosla","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref10","article-title":"Joint distribution optimal transportation for domain adaptation","volume":"30","author":"courty","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref54","first-page":"1","article-title":"Unsupervised learning of visual features by contrasting cluster assignments","author":"caron","year":"2020","journal-title":"Proc 34th Conf Neural Inf Process Syst (NIPS)"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00851"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01237-3_9"},{"key":"ref19","first-page":"1081","article-title":"Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation","author":"chen","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01272"},{"key":"ref51","article-title":"Contrastive multiview coding","author":"tian","year":"2019","journal-title":"arXiv 1906 05849"},{"key":"ref50","first-page":"15509","article-title":"Learning representations by maximizing mutual information across views","author":"bachman","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref46","article-title":"Domain adversarial reinforcement learning for partial domain adaptation","author":"chen","year":"2020","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.2983337"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2992393"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58621-8_8"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.01004"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00145"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2964173"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00310"},{"key":"ref49","first-page":"1","article-title":"Learning deep representations by mutual information estimation and maximization","author":"hjelm","year":"2019","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref8","first-page":"2839","article-title":"Domain adaptation with conditional transferable components","author":"gong","year":"2016","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3016180"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00150"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/11171.001.0001"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3400066"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","article-title":"Domain adaptation via transfer component analysis","volume":"22","author":"pan","year":"2010","journal-title":"IEEE Trans Neural Netw"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00503"},{"key":"ref34","first-page":"136","article-title":"Unsupervised domain adaptation with residual transfer networks","author":"long","year":"2016","journal-title":"Proc 30th Int Conf Neural Inf Process Syst"},{"key":"ref37","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc NIPS"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00362"},{"key":"ref31","first-page":"3320","article-title":"How transferable are features in deep neural networks?","volume":"27","author":"yosinski","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.547"},{"key":"ref33","first-page":"97","article-title":"Learning transferable features with deep adaptation networks","author":"long","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-13560-1_76"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2995800"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.3390\/sym12030427"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00400"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11784"},{"key":"ref24","first-page":"1","article-title":"Central moment discrepancy (CMD) for domain-invariant representation learning","author":"zellinger","year":"2017","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.183"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOM.2018.8444572"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.274"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.3390\/technologies9010002"},{"key":"ref64","first-page":"6028","article-title":"Do we really need to access the source data? Source hypothesis transfer for unsupervised domain adaptation","author":"liang","year":"2020","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref63","first-page":"1647","article-title":"Conditional adversarial domain adaptation","author":"long","year":"2018","journal-title":"Proc 32nd Int Conf Neural Inf Process Syst (NIPS)"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-58347-1_8"},{"key":"ref66","first-page":"1","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.114"},{"key":"ref65","first-page":"2208","article-title":"Deep transfer learning with joint adaptation networks","author":"long","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2017.150"},{"key":"ref27","first-page":"2988","article-title":"Asymmetric tri-training for unsupervised domain adaptation","author":"saito","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-016-0944-x"},{"key":"ref60","article-title":"Caltech-256 object category dataset","author":"griffin","year":"2007"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref61","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10273172\/09705553.pdf?arnumber=9705553","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T18:14:34Z","timestamp":1698084874000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9705553\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10]]},"references-count":66,"journal-issue":{"issue":"10"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2022.3145034","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10]]}}}