{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T03:09:58Z","timestamp":1775099398926,"version":"3.50.1"},"reference-count":222,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Berkeley DeepDrive"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2022,2]]},"DOI":"10.1109\/tnnls.2020.3028503","type":"journal-article","created":{"date-parts":[[2020,10,23]],"date-time":"2020-10-23T19:27:58Z","timestamp":1603481278000},"page":"473-493","source":"Crossref","is-referenced-by-count":278,"title":["A Review of Single-Source Deep Unsupervised Visual Domain Adaptation"],"prefix":"10.1109","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5843-6411","authenticated-orcid":false,"given":"Sicheng","family":"Zhao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6887-2046","authenticated-orcid":false,"given":"Xiangyu","family":"Yue","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4047-3526","authenticated-orcid":false,"given":"Shanghang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Bo","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1158-0779","authenticated-orcid":false,"given":"Han","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Bichen","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Ravi","family":"Krishna","sequence":"additional","affiliation":[]},{"given":"Joseph E.","family":"Gonzalez","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1298-8389","authenticated-orcid":false,"given":"Alberto L.","family":"Sangiovanni-Vincentelli","sequence":"additional","affiliation":[]},{"given":"Sanjit A.","family":"Seshia","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3868-8501","authenticated-orcid":false,"given":"Kurt","family":"Keutzer","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref170","article-title":"Caltech-256 object category dataset","author":"griffin","year":"2007"},{"key":"ref172","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.789"},{"key":"ref171","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref174","article-title":"Domain adaptation: Learning bounds and algorithms","author":"mansour","year":"2009","journal-title":"Proc COLT"},{"key":"ref173","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"},{"key":"ref176","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-011-5268-1"},{"key":"ref175","doi-asserted-by":"publisher","DOI":"10.1007\/s10472-013-9391-5"},{"key":"ref178","first-page":"737","article-title":"Signature verification using a &#x2018;Siamese&#x2019; time delay neural network","author":"bromley","year":"1994","journal-title":"Proc NeurIPS"},{"key":"ref177","first-page":"367","article-title":"Multiple source adaptation and the r&#x00E9;nyi divergence","author":"mansour","year":"2009","journal-title":"Proc UAI"},{"key":"ref168","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.352"},{"key":"ref169","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.161"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2014.12.003"},{"key":"ref38","article-title":"Transfer adaptation learning: A decade survey","author":"zhang","year":"2019","journal-title":"arXiv 1903 04687"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.368"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.95"},{"key":"ref31","first-page":"2066","article-title":"Geodesic flow kernel for unsupervised domain adaptation","author":"gong","year":"2012","journal-title":"Proc CVPR"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126344"},{"key":"ref37","article-title":"A review of domain adaptation without target labels","author":"kouw","year":"2019","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3123266.3123292"},{"key":"ref35","first-page":"342","article-title":"Graph matching and pseudo-label guided deep unsupervised domain adaptation","author":"das","year":"2018","journal-title":"Proc ICANN"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.249"},{"key":"ref181","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc NeurIPS"},{"key":"ref180","first-page":"469","article-title":"Coupled generative adversarial networks","author":"liu","year":"2016","journal-title":"Proc NeurIPS"},{"key":"ref185","first-page":"527","article-title":"Support and invertibility in domain-invariant representations","author":"johansson","year":"2019","journal-title":"Proc AISTATS"},{"key":"ref184","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.293"},{"key":"ref183","article-title":"Auto-encoding variational Bayes","author":"kingma","year":"2014","journal-title":"Proc ICLR"},{"key":"ref182","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref189","first-page":"1691","article-title":"Unsupervised domain adaptation with a relaxed covariate shift assumption","author":"adel","year":"2017","journal-title":"Proc AAAI"},{"key":"ref188","first-page":"1147","article-title":"Analysis of kernel mean matching under covariate shift","author":"yu","year":"2012","journal-title":"Proc ICML"},{"key":"ref187","first-page":"5","article-title":"Covariate shift by kernel mean matching","volume":"3","author":"gretton","year":"2009","journal-title":"Dataset Shift Mach Learn"},{"key":"ref186","first-page":"985","article-title":"Covariate shift adaptation by importance weighted cross validation","volume":"8","author":"sugiyama","year":"2007","journal-title":"J Mach Learn Res"},{"key":"ref28","first-page":"601","article-title":"Correcting sample selection bias by unlabeled data","author":"huang","year":"2007","journal-title":"Proc NIPS"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-58347-1_1"},{"key":"ref179","article-title":"Siamese neural networks for one-shot image recognition","author":"koch","year":"2015","journal-title":"Proc ICML Deep Learn Workshop"},{"key":"ref29","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 ICML"},{"key":"ref20","article-title":"Momentum contrast for unsupervised visual representation learning","author":"he","year":"2019","journal-title":"arXiv 1911 05722"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.572"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.241"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2014.2347059"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_7"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5745"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00503"},{"key":"ref154","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00566"},{"key":"ref153","first-page":"3490","article-title":"Learning to generalize: Meta-learning for domain generalization","author":"li","year":"2018","journal-title":"Proc AAAI"},{"key":"ref156","first-page":"1180","article-title":"Unsupervised domain adaptation by backpropagation","author":"ganin","year":"2015","journal-title":"Proc ICML"},{"key":"ref155","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref150","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6997"},{"key":"ref152","first-page":"10","article-title":"Domain generalization via invariant feature representation","author":"muandet","year":"2013","journal-title":"Proc ICML"},{"key":"ref151","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i03.5651"},{"key":"ref146","first-page":"8568","article-title":"Adversarial multiple source domain adaptation","author":"zhao","year":"2018","journal-title":"Proc NeurIPS"},{"key":"ref147","first-page":"8246","article-title":"Algorithms and theory for multiple-source adaptation","author":"hoffman","year":"2018","journal-title":"Proc NeurIPS"},{"key":"ref148","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00149"},{"key":"ref149","first-page":"7285","article-title":"Multi-source domain adaptation for semantic segmentation","author":"zhao","year":"2019","journal-title":"Proc NeurIPS"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.220"},{"key":"ref58","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","article-title":"FCNs in the wild: Pixel-level adversarial and constraint-based adaptation","author":"hoffman","year":"2016","journal-title":"arXiv 1612 02649"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00970"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01053"},{"key":"ref54","first-page":"5077","article-title":"AutoDIAL: Automatic domain alignment layers","author":"cariucci","year":"2017","journal-title":"Proc ICCV"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.03.005"},{"key":"ref52","first-page":"2208","article-title":"Deep transfer learning with joint adaptation networks","author":"long","year":"2017","journal-title":"Proc ICML"},{"key":"ref40","article-title":"Multi-source domain adaptation in the deep learning era: A systematic survey","author":"zhao","year":"2020","journal-title":"arXiv 2002 12169"},{"key":"ref167","article-title":"BDD100K: A diverse driving dataset for heterogeneous multitask learning","author":"yu","year":"2018","journal-title":"arXiv 1805 04687"},{"key":"ref166","article-title":"Syn2Real: A new benchmark forSynthetic-to-Real visual domain adaptation","author":"peng","year":"2018","journal-title":"arXiv 1806 09755"},{"key":"ref165","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989092"},{"key":"ref164","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-018-1072-8"},{"key":"ref163","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref162","doi-asserted-by":"publisher","DOI":"10.1145\/2629500"},{"key":"ref161","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299081"},{"key":"ref160","article-title":"VisDA: The visual domain adaptation challenge","author":"peng","year":"2017","journal-title":"arXiv 1710 06924"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8462926"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3240508.3240591"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3206025.3206080"},{"key":"ref8","first-page":"7124","article-title":"Towards accurate model selection in deep unsupervised domain adaptation","volume":"2019","author":"you","year":"0","journal-title":"Proc ICML"},{"key":"ref159","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33012620"},{"key":"ref49","article-title":"Central moment discrepancy (CMD) for domain-invariant representation learning","author":"zellinger","year":"2017","journal-title":"Proc ICLR"},{"key":"ref157","doi-asserted-by":"publisher","DOI":"10.1109\/34.291440"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"jialin pan","year":"2010","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"ref158","article-title":"Reading digits in natural images with unsupervised feature learning","author":"netzer","year":"2011","journal-title":"Proc NeurIPS Workshops"},{"key":"ref46","first-page":"97","article-title":"Learning transferable features with deep adaptation networks","author":"long","year":"2015","journal-title":"Proc ICML"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00352"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2814042"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-58347-1_8"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01424-7_27"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.05.083"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8793495"},{"key":"ref43","article-title":"A survey of unsupervised deep domain adaptation","author":"wilson","year":"2018","journal-title":"arXiv 1812 02849"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00887"},{"key":"ref72","first-page":"401","article-title":"Deep adversarial attention alignment for unsupervised domain adaptation: The benefit of target expectation maximization","author":"kang","year":"2018","journal-title":"Proc ECCV"},{"key":"ref71","first-page":"1994","article-title":"CyCADA: Cycle-consistent adversarial domain adaptation","author":"hoffman","year":"2018","journal-title":"Proc ICML"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.18"},{"key":"ref76","first-page":"518","article-title":"Dcan: Dual channel-wise alignment networks for unsupervised scene adaptation","author":"wu","year":"2018","journal-title":"Proc ECCV"},{"key":"ref77","first-page":"700","article-title":"Unsupervised image-to-image translation networks","author":"liu","year":"2017","journal-title":"Proc NeurIPS"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00278"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1145\/3343031.3350902"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00845"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_36"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00145"},{"key":"ref62","first-page":"1640","article-title":"Conditional adversarial domain adaptation","author":"long","year":"2018","journal-title":"Proc NeurIPS"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00780"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00150"},{"key":"ref64","article-title":"PANDA: Prototypical unsupervised domain adaptation","author":"hu","year":"2020","journal-title":"arXiv 2003 13274"},{"key":"ref65","first-page":"4013","article-title":"Transferable adversarial training: A general approach to adapting deep classifiers","author":"liu","year":"2019","journal-title":"Proc ICML"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6123"},{"key":"ref67","article-title":"Dual adversarial domain adaptation","author":"du","year":"2020","journal-title":"arXiv 2001 00153"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5757"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00234"},{"key":"ref197","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.109"},{"key":"ref198","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2012.2199502"},{"key":"ref199","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2655035"},{"key":"ref193","article-title":"Federated adversarial domain adaptation","author":"peng","year":"2020","journal-title":"Proc ICLR"},{"key":"ref194","first-page":"5102","article-title":"Domain agnostic learning with disentangled representations","author":"peng","year":"2019","journal-title":"Proc ICML"},{"key":"ref195","first-page":"35","article-title":"Diverse image-to-image translation via disentangled representations","author":"lee","year":"2018","journal-title":"Proc ECCV"},{"key":"ref196","doi-asserted-by":"publisher","DOI":"10.1109\/T-AFFC.2011.25"},{"key":"ref95","first-page":"289","article-title":"Unsupervised domain adaptation for semantic segmentation via class-balanced self-training","author":"zou","year":"2018","journal-title":"Proc ECCV"},{"key":"ref94","article-title":"Regularized learning for domain adaptation under label shifts","author":"azizzadenesheli","year":"2019","journal-title":"Proc ICLR"},{"key":"ref190","first-page":"616","article-title":"On target shift in adversarial domain adaptation","author":"li","year":"2019","journal-title":"Proc AISTATS"},{"key":"ref93","article-title":"Generalized domain adaptation with covariate and label shift co-alignment","author":"tan","year":"2019","journal-title":"arXiv 1910 10320"},{"key":"ref191","first-page":"177","article-title":"Transferable meta learning across domains","author":"kang","year":"2018","journal-title":"Proc UAI"},{"key":"ref92","first-page":"3122","article-title":"Detecting and correcting for label shift with black box predictors","author":"lipton","year":"2018","journal-title":"Proc ICML"},{"key":"ref192","first-page":"998","article-title":"MetaReg: Towards domain generalization using meta-regularization","author":"balaji","year":"2018","journal-title":"Proc NeurIPS"},{"key":"ref91","article-title":"Domain adaptation with conditional distribution matching and generalized label shift","author":"tachet des combes","year":"2020","journal-title":"arXiv 2003 04475"},{"key":"ref90","first-page":"7523","article-title":"On learning invariant representations for domain adaptation","author":"zhao","year":"2019","journal-title":"Proc ICML"},{"key":"ref98","first-page":"1195","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","author":"tarvainen","year":"2017","journal-title":"Proc NeurIPS"},{"key":"ref99","article-title":"Self-ensembling for visual domain adaptation","author":"french","year":"2018","journal-title":"Proc ICLR"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00608"},{"key":"ref97","article-title":"Temporal ensembling for semi-supervised learning","author":"laine","year":"2017","journal-title":"Proc ICLR"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00233"},{"key":"ref81","article-title":"Unsupervised domain adaptation through self-supervision","author":"sun","year":"2019","journal-title":"arXiv 1909 11825"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00334"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2949697"},{"key":"ref80","first-page":"343","article-title":"Domain separation networks","author":"bousmalis","year":"2016","journal-title":"Proc NeurIPS"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00473"},{"key":"ref85","article-title":"Cross-domain self-supervised learning for domain adaptation with few source labels","author":"kim","year":"2020","journal-title":"arXiv 2003 08264"},{"key":"ref86","article-title":"Self-supervised learning for domain adaptation on point-clouds","author":"achituve","year":"2020","journal-title":"arXiv 2003 12641"},{"key":"ref87","article-title":"SPLAT: Semantic pixel-level adaptation transforms for detection","author":"tzeng","year":"2018","journal-title":"arXiv 1812 00929"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00712"},{"key":"ref200","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"2017","journal-title":"Proc ICLR"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.223"},{"key":"ref100","article-title":"There are many consistent explanations of unlabeled data: Why you should average","author":"athiwaratkun","year":"2019","journal-title":"Proc ICLR"},{"key":"ref209","article-title":"Private federated learning with domain adaptation","author":"peterson","year":"2019","journal-title":"arXiv 1912 06733"},{"key":"ref203","first-page":"3987","article-title":"Continual learning through synaptic intelligence","author":"zenke","year":"2017","journal-title":"Proc ICML"},{"key":"ref204","first-page":"4652","article-title":"Overcoming catastrophic forgetting by incremental moment matching","author":"lee","year":"2017","journal-title":"Proc NeurIPS"},{"key":"ref201","doi-asserted-by":"publisher","DOI":"10.1145\/2523813"},{"key":"ref202","doi-asserted-by":"crossref","first-page":"3521","DOI":"10.1073\/pnas.1611835114","article-title":"Overcoming catastrophic forgetting in neural networks","volume":"114","author":"james","year":"2017","journal-title":"Proc Nat Acad Sci USA"},{"key":"ref207","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00219"},{"key":"ref208","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01299"},{"key":"ref205","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00221"},{"key":"ref206","article-title":"Dataset distillation","author":"wang","year":"2018","journal-title":"arXiv 1811 10959"},{"key":"ref211","article-title":"Analyzing inverse problems with invertible neural networks","author":"ardizzone","year":"2019","journal-title":"Proc ICLR"},{"key":"ref210","article-title":"I-RevNet: Deep invertible networks","author":"jacobsen","year":"2018","journal-title":"Proc ICLR"},{"key":"ref212","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2017.8202133"},{"key":"ref213","first-page":"613","article-title":"Generating videos with scene dynamics","author":"vondrick","year":"2016","journal-title":"Proc NeurIPS"},{"key":"ref214","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00165"},{"key":"ref215","first-page":"1144","article-title":"Video-to-video synthesis","author":"wang","year":"2018","journal-title":"Proc NeurIPS"},{"key":"ref216","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01267-0_11"},{"key":"ref217","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2014.2330816"},{"key":"ref218","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2011.941851"},{"key":"ref219","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/780"},{"key":"ref220","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013240"},{"key":"ref222","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01099"},{"key":"ref221","first-page":"5276","article-title":"Towards fast computation of certified robustness for Relu networks","author":"weng","year":"2018","journal-title":"Proc ICML"},{"key":"ref127","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00712"},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00677"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00078"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00057"},{"key":"ref129","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00401"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01274"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093358"},{"key":"ref133","article-title":"Semi-supervised learning methods for unsupervised domain adaptation in medical imaging segmentation","author":"ballester","year":"2019"},{"key":"ref134","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00814"},{"key":"ref131","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01378"},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.463"},{"key":"ref136","first-page":"505","article-title":"A two-stage weighting framework for multi-source domain adaptation","author":"sun","year":"2011","journal-title":"Proc NeurIPS"},{"key":"ref135","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553411"},{"key":"ref138","doi-asserted-by":"publisher","DOI":"10.1145\/2382577.2382582"},{"key":"ref137","first-page":"1338","article-title":"Exploiting Web images for event recognition in consumer videos: A multiple source domain adaptation approach","author":"duan","year":"2012","journal-title":"Proc CVPR"},{"key":"ref139","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2011.2178556"},{"key":"ref140","doi-asserted-by":"publisher","DOI":"10.1145\/1291233.1291276"},{"key":"ref141","first-page":"1433","article-title":"An empirical analysis of domain adaptation algorithms for genomic sequence analysis","author":"schweikert","year":"2009","journal-title":"Proc NeurIPS"},{"key":"ref142","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-34487-9_41"},{"key":"ref143","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLC.2013.6890438"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.151"},{"key":"ref144","first-page":"3691","article-title":"Multi-source iterative adaptation for cross-domain classification","author":"bhatt","year":"2016","journal-title":"Proc IJCAI"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"ref145","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00417"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.549"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.167"},{"key":"ref107","first-page":"1541","article-title":"Heterogeneous domain adaptation using manifold alignment","author":"wang","year":"2011","journal-title":"Proc IJCAI"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014951"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2910667"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00823"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00392"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1145\/3240508.3240512"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2913723"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1145\/3343031.3350955"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2868854"},{"key":"ref10","first-page":"165","article-title":"Label efficient learning of transferable representations acrosss domains and tasks","author":"luo","year":"2017","journal-title":"Proc NeurIPS"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref12","first-page":"2152","article-title":"An embarrassingly simple approach to zero-shot learning","author":"romera-paredes","year":"2015","journal-title":"Proc ICML"},{"key":"ref13","first-page":"6143","article-title":"Dual adversarial semantics-consistent network for generalized zero-shot learning","author":"ni","year":"2019","journal-title":"Proc NeurIPS"},{"key":"ref14","first-page":"935","article-title":"Zero-shot learning through cross-modal transfer","author":"socher","year":"2013","journal-title":"Proc NeurIPS"},{"key":"ref15","article-title":"Generalized zero-shot ICD coding","author":"song","year":"2019","journal-title":"arXiv 1909 13154"},{"key":"ref16","first-page":"4077","article-title":"Prototypical networks for few-shot learning","author":"snell","year":"2017","journal-title":"Proc NeurIPS"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00288"},{"key":"ref17","article-title":"Optimization as a model for few-shot learning","author":"ravi","year":"2017","journal-title":"Proc ICLR"},{"key":"ref117","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00304"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref19","article-title":"A simple framework for contrastive learning of visual representations","author":"chen","year":"2020","journal-title":"arXiv 2002 05709"},{"key":"ref119","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00851"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2963389"},{"key":"ref113","article-title":"Multi-target unsupervised domain adaptation without exactly shared categories","author":"yu","year":"2018","journal-title":"arXiv 1809 00852"},{"key":"ref116","first-page":"153","article-title":"Open set domain adaptation by backpropagation","author":"saito","year":"2018","journal-title":"Proc ECCV"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.88"},{"key":"ref120","first-page":"135","article-title":"Partial adversarial domain adaptation","author":"cao","year":"2018","journal-title":"Proc ECCV"},{"key":"ref121","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00310"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00283"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01172"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/9702897\/09238468.pdf?arnumber=9238468","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T20:47:14Z","timestamp":1650919634000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9238468\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2]]},"references-count":222,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2020.3028503","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2]]}}}