{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:52:43Z","timestamp":1775667163459,"version":"3.50.1"},"reference-count":76,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/tpami.2023.3298332","type":"journal-article","created":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T17:43:07Z","timestamp":1690220587000},"page":"1-18","source":"Crossref","is-referenced-by-count":27,"title":["Source Free Semi-Supervised Transfer Learning for Diagnosis of Mental Disorders on fMRI Scans"],"prefix":"10.1109","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5477-8753","authenticated-orcid":false,"given":"Yao","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhi-An","family":"Huang","sequence":"additional","affiliation":[{"name":"Research Office, City University of Hong Kong (Dongguan), Dongguan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1926-3321","authenticated-orcid":false,"given":"Rui","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6836-7245","authenticated-orcid":false,"given":"Xiaoming","family":"Xue","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1386-6853","authenticated-orcid":false,"given":"Xiaoyan","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2756-4984","authenticated-orcid":false,"given":"Linqi","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong"}],"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, Hong Kong Polytechnic University, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2582924"},{"key":"ref57","first-page":"1","article-title":"Convolutional deep belief networks on CIFAR-10","volume":"40","author":"krizhevsky","year":"2010","journal-title":"Unpublished manuscript"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.3389\/fnsys.2012.00069"},{"key":"ref56","first-page":"2089","article-title":"Exploiting shared representations for personalized federated learning","author":"collins","year":"2021","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2016.10.045"},{"key":"ref59","first-page":"18661","article-title":"Supervised contrastive learning","author":"khosla","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3104474"},{"key":"ref58","first-page":"1","article-title":"Not all knowledge is created equal: Mutual distillation of confident knowledge","author":"li","year":"2022","journal-title":"Proc Annu Conf Neural Inf Process Syst"},{"key":"ref53","first-page":"1","article-title":"A DIRT-T approach to unsupervised domain adaptation","author":"shu","year":"2018","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/IAI50351.2020.9262176"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2022.3210940"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1038\/mp.2013.78"},{"key":"ref54","first-page":"1647","article-title":"Conditional adversarial domain adaptation","author":"long","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2933160"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2987817"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101765"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2824309"},{"key":"ref51","first-page":"2445","article-title":"Information maximization for few-shot learning","volume":"33","author":"boudiaf","year":"2020","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2621761"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2020.00206"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-019-7469-8"},{"key":"ref48","article-title":"When does label smoothing help?","volume":"32","author":"m\u00fcller","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref47","article-title":"4D spatio-temporal deep learning with 4D fMRI data for autism spectrum disorder classification","author":"bengs","year":"2020"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci10070463"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2018.08.005"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2018.00491"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.21333"},{"key":"ref49","first-page":"1558","article-title":"Learning discrete representations via information maximizing self-augmented training","author":"hu","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-10933-3"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1377\/hlthaff.2014.0147"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging7040066"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3125686"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2511754"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3031898"},{"key":"ref5","first-page":"5042","article-title":"Learning deep sparse regularizers with applications to multi-view clustering and semi-supervised classification","volume":"44","author":"wang","year":"2022","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32692-0_31"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59713-9_36"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s11682-015-9356-x"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2015.06.010"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2019.8856726"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI52829.2022.9761681"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2022.3209345"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICMEW.2019.00009"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3129809"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2945942"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966432"},{"key":"ref76","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Proc Artif Intell Statist"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3007943"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s11920-017-0780-z"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.3389\/fncom.2020.00019"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2859478"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.02004"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/213"},{"key":"ref73","first-page":"2579","article-title":"Visualizing data using t-SNE.","volume":"9","author":"van der maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16452-1_12"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-47436-2_31"},{"key":"ref68","first-page":"1","article-title":"In defense of pseudo-labeling: An uncertainty-aware pseudo-label selection framework for semi-supervised learning","author":"rizve","year":"2021","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-019-05855-6"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00685"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59710-8_59"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/BCI48061.2020.9061617"},{"key":"ref69","first-page":"1","article-title":"Con$^{2}$2DA: Simplifying semi-supervised domain adaptation by learning consistent and contrastive feature representations","author":"p\u00e9rez-carrasco","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref20","first-page":"8602","article-title":"Source data-absent unsupervised domain adaptation through hypothesis transfer and labeling transfer","volume":"44","author":"liang","year":"2022","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref64","first-page":"41","article-title":"The neuro bureau preprocessing initiative: Open sharing of preprocessed neuroimaging data and derivatives","volume":"7","author":"craddock","year":"2013","journal-title":"Front Neuroinform"},{"key":"ref63","first-page":"3030","article-title":"Learning what and where to transfer","author":"jang","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2020.10.006"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2018.8363676"},{"key":"ref21","first-page":"4544","article-title":"Universal source-free domain adaptation","author":"kundu","year":"2020","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.3389\/fnsys.2010.00013"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2019.8759585"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1117\/12.2548630"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87196-3_46"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00454"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2953131"},{"key":"ref61","first-page":"1","article-title":"Mutual mean-teaching: Pseudo label refinery for unsupervised domain adaptation on person re-identification","author":"ge","year":"2020","journal-title":"Proc Int Conf Learn Representations"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/4359286\/10192368.pdf?arnumber=10192368","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,4]],"date-time":"2023-10-04T15:11:31Z","timestamp":1696432291000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10192368\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":76,"URL":"https:\/\/doi.org\/10.1109\/tpami.2023.3298332","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}