{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T10:23:01Z","timestamp":1778149381779,"version":"3.51.4"},"reference-count":58,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"European Union through the FUI Pikaflex Project within the FEDER Program"},{"name":"French Research Agency, l\u2019Agence Nationale de Recherche (ANR), through the LabCom Ar\u00e8s Project","award":["ANR16-LCV2-0012-01"],"award-info":[{"award-number":["ANR16-LCV2-0012-01"]}]},{"name":"ERA-CHIST LEARN-REAL Project","award":["ANR-18-CHR3-0002-01"],"award-info":[{"award-number":["ANR-18-CHR3-0002-01"]}]},{"DOI":"10.13039\/501100001809","name":"Beijing Advanced Innovation Center for Big data and Brain Computing, Beihang University and the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61773262"],"award-info":[{"award-number":["61773262"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"China Aviation Science Foundation","award":["20142057006"],"award-info":[{"award-number":["20142057006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Cybern."],"published-print":{"date-parts":[[2020,9]]},"DOI":"10.1109\/tcyb.2019.2962000","type":"journal-article","created":{"date-parts":[[2020,1,17]],"date-time":"2020-01-17T21:09:08Z","timestamp":1579295348000},"page":"3914-3927","source":"Crossref","is-referenced-by-count":53,"title":["Discriminative and Geometry-Aware Unsupervised Domain Adaptation"],"prefix":"10.1109","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3300-5695","authenticated-orcid":false,"given":"Lingkun","family":"Luo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liming","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9362-4642","authenticated-orcid":false,"given":"Shiqiang","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9921-7933","authenticated-orcid":false,"given":"Ying","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5603-6411","authenticated-orcid":false,"given":"Xiaofang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2814042"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2014.2347059"},{"key":"ref33","article-title":"Close yet distinctive domain adaptation","volume":"abs 1704 4235","author":"luo","year":"2017","journal-title":"CoRR"},{"key":"ref32","article-title":"Close yet distinctive domain adaptation","author":"luo","year":"2017","journal-title":"arXiv preprint arXiv 1704 04235"},{"key":"ref31","article-title":"Robust data geometric structure aligned close yet discriminative domain adaptation","author":"luo","year":"2017","journal-title":"arXiv preprint arXiv 1705 08620"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.183"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.88"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2091281"},{"key":"ref34","first-page":"849","article-title":"On spectral clustering: Analysis and an algorithm","author":"ng","year":"2002","journal-title":"Advances in Neural Information Processing Systems 14"},{"key":"ref28","first-page":"97","article-title":"Learning transferable features with deep adaptation networks","author":"long","year":"2015","journal-title":"Proc ICML"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.274"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"},{"key":"ref1","first-page":"1","article-title":"Distribution-matching embedding for visual domain adaptation","volume":"17","author":"baktashmotlagh","year":"2016","journal-title":"J Mach Learn Res"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2016.2609820"},{"key":"ref22","first-page":"2168","article-title":"Robust visual domain adaptation with low-rank reconstruction","author":"jhuo","year":"2012","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit (CVPR)"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/34.291440"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/B978-012088469-8.50019-X"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2018.2839583"},{"key":"ref26","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"1690","DOI":"10.1109\/TPAMI.2012.237","article-title":"Learning full pairwise affinities for spectral segmentation","volume":"35","author":"kim","year":"2013","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.572"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.572"},{"key":"ref58","first-page":"321","article-title":"Learning with local and global consistency","author":"zhou","year":"2004","journal-title":"Advances in Neural Information Processing Systems 16"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.547"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2015.2510498"},{"key":"ref55","first-page":"5419","article-title":"Learning semantic representations for unsupervised domain adaptation","author":"xie","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1145\/3240508.3240512"},{"key":"ref53","first-page":"2099","article-title":"Cross-domain metric learning based on information theory","author":"wang","year":"2014","journal-title":"Proc 28th AAAI Conf Artif Intell"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00576"},{"key":"ref10","first-page":"647","article-title":"DeCAF: A deep convolutional activation feature for generic visual recognition","author":"donahue","year":"2014","journal-title":"Proc of the 31th Intl Conf on Mach Learn (ICML)"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.368"},{"key":"ref40","first-page":"2988","article-title":"Asymmetric tri-training for unsupervised domain adaptation","author":"saito","year":"2017","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref12","volume":"15","author":"fortin","year":"2000","journal-title":"Augmented Lagrangian Methods Applications to the Numerical Solution of Boundary-Value Problems"},{"key":"ref13","first-page":"2096","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"ganin","year":"2016","journal-title":"J Mach Learn Res"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2599532"},{"key":"ref15","first-page":"222","article-title":"Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation","author":"gong","year":"2013","journal-title":"Proc 30th Int Conf Mach Learn (ICML)"},{"key":"ref16","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 (CVPR)"},{"key":"ref17","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref18","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"gretton","year":"2012","journal-title":"J Mach Learn Res"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.421"},{"key":"ref4","first-page":"343","article-title":"Domain separation networks","author":"bousmalis","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btl242"},{"key":"ref6","article-title":"Marginalized denoising autoencoders for domain adaptation","author":"chen","year":"2012","journal-title":"arXiv preprint arXiv 1206 4683"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2007.4408856"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2016.2631887"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-58347-1_1"},{"key":"ref49","article-title":"Deep domain confusion: Maximizing for domain invariance","volume":"abs 1412 3474","author":"tzeng","year":"2014","journal-title":"CoRR"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.92"},{"key":"ref46","first-page":"8","article-title":"Return of frustratingly easy domain adaptation","volume":"6","author":"sun","year":"2016","journal-title":"Proc AAAI"},{"key":"ref45","first-page":"1433","article-title":"Direct importance estimation with model selection and its application to covariate shift adaptation","author":"sugiyama","year":"2008","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref48","article-title":"Deep domain confusion: Maximizing for domain invariance","author":"tzeng","year":"2014","journal-title":"Arxiv preprint arXiv 1412 3474"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2771779"},{"key":"ref42","first-page":"2110","article-title":"Learning transferrable representations for unsupervised domain adaptation","author":"sener","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1162\/089976698300017467"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.126"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-014-0696-6"}],"container-title":["IEEE Transactions on Cybernetics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6221036\/9170745\/08961922.pdf?arnumber=8961922","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T17:21:03Z","timestamp":1651080063000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8961922\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9]]},"references-count":58,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tcyb.2019.2962000","relation":{},"ISSN":["2168-2267","2168-2275"],"issn-type":[{"value":"2168-2267","type":"print"},{"value":"2168-2275","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9]]}}}