{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T15:26:59Z","timestamp":1780586819821,"version":"3.54.1"},"reference-count":63,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100006465","name":"Korea Creative Content Agency","doi-asserted-by":"publisher","award":["RS-2024-00439534"],"award-info":[{"award-number":["RS-2024-00439534"]}],"id":[{"id":"10.13039\/501100006465","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["NRF-2022R1A2C4002052"],"award-info":[{"award-number":["NRF-2022R1A2C4002052"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010418","name":"Institute for Information and Communications Technology Promotion","doi-asserted-by":"publisher","award":["IITP-2025-RS-2020-II201460"],"award-info":[{"award-number":["IITP-2025-RS-2020-II201460"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Image Process."],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tip.2025.3570220","type":"journal-article","created":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T17:42:43Z","timestamp":1747849363000},"page":"3181-3193","source":"Crossref","is-referenced-by-count":3,"title":["Label Space-Induced Pseudo Label Refinement for Multi-Source Black-Box Domain Adaptation"],"prefix":"10.1109","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9880-2850","authenticated-orcid":false,"given":"Chaehwa","family":"Yoo","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Chungbuk National University, Cheongju, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4514-2016","authenticated-orcid":false,"given":"Xiaofeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0517-0952","authenticated-orcid":false,"given":"Fangxu","family":"Xing","sequence":"additional","affiliation":[{"name":"Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5621-9218","authenticated-orcid":false,"given":"Jonghye","family":"Woo","sequence":"additional","affiliation":[{"name":"Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1637-9479","authenticated-orcid":false,"given":"Je-Won","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Electronic and Electrical Engineering and the Graduate Program in Smart Factory, Ewha Womans University, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00997"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.18"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_9"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2950768"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7503.003.0045"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00408"},{"key":"ref8","first-page":"2848","article-title":"Confident anchor-induced multi-source free domain adaptation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Dong"},{"key":"ref9","first-page":"1","article-title":"An image is worth 16\u00d716 words: Transformers for image recognition at scale","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Dosovitskiy"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3056212"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.5555\/2946645.2946704"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref13","first-page":"1","article-title":"Algorithms and theory for multiple-source adaptation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hoffman"},{"key":"ref14","first-page":"1989","article-title":"CyCADA: Cycle-consistent adversarial domain adaptation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Hoffman"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3124674"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3109859.3109861"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2024.3353539"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01053"},{"issue":"2","key":"ref19","first-page":"896","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","volume":"3","author":"Lee"},{"key":"ref20","first-page":"6799","article-title":"Extracting relationships by multi-domain matching","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Li"},{"key":"ref21","first-page":"6028","article-title":"Do we really need to access the source data? Source hypothesis transfer for unsupervised domain adaptation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Liang"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3103390"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00784"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2022.3194414"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1117\/12.2607895"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1561\/116.00000192"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.274"},{"key":"ref28","first-page":"97","article-title":"Learning transferable features with deep adaptation networks","volume-title":"Proc. 32nd Int. Conf. Mach. Learn.","volume":"37","author":"Long"},{"key":"ref29","first-page":"2208","article-title":"Deep transfer learning with joint adaptation networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Long"},{"key":"ref30","first-page":"1","article-title":"Conditional adversarial domain adaptation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Long"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3295929"},{"key":"ref32","first-page":"1","article-title":"Domain adaptation with multiple sources","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"21","author":"Mansour"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00234"},{"key":"ref34","article-title":"Automatic differentiation in PyTorch","author":"Paszke","year":"2017"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3258753"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11767"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00149"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/402"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3152052"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00392"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00887"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref45","first-page":"4647","article-title":"Your classifier can secretly suffice multi-source domain adaptation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Venkat"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.572"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58598-3_43"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1145\/3240508.3240512"},{"key":"ref49","article-title":"Theoretical analysis of self-training with deep networks on unlabeled data","author":"Wei","year":"2020","journal-title":"arXiv:2010.03622"},{"key":"ref50","first-page":"5423","article-title":"Learning semantic representations for unsupervised domain adaptation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xie"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01237"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6123"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00417"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.107"},{"key":"ref55","article-title":"Divide to adapt: Mitigating confirmation bias for domain adaptation of black-box predictors","author":"Yang","year":"2022","journal-title":"arXiv:2205.14467"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00874"},{"key":"ref57","article-title":"Unsupervised domain adaptation of black-box source models","author":"Zhang","year":"2021","journal-title":"arXiv:2101.02839"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.547"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00400"},{"key":"ref60","first-page":"8559","article-title":"Adversarial multiple source domain adaptation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Zhao"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015989"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_18"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3065254"}],"container-title":["IEEE Transactions on Image Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/83\/10795784\/11008464.pdf?arnumber=11008464","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T05:53:39Z","timestamp":1748930019000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11008464\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":63,"URL":"https:\/\/doi.org\/10.1109\/tip.2025.3570220","relation":{},"ISSN":["1057-7149","1941-0042"],"issn-type":[{"value":"1057-7149","type":"print"},{"value":"1941-0042","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}