{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T04:13:49Z","timestamp":1780373629585,"version":"3.54.1"},"reference-count":72,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100002367","name":"Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["XDB0680202"],"award-info":[{"award-number":["XDB0680202"]}],"id":[{"id":"10.13039\/501100002367","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62306301"],"award-info":[{"award-number":["62306301"]}],"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":["62376259"],"award-info":[{"award-number":["62376259"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012152","name":"National Postdoctoral Program for Innovative Talents","doi-asserted-by":"publisher","award":["BX20220310"],"award-info":[{"award-number":["BX20220310"]}],"id":[{"id":"10.13039\/501100012152","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1109\/tpami.2024.3404450","type":"journal-article","created":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T17:48:24Z","timestamp":1717091304000},"page":"8056-8073","source":"Crossref","is-referenced-by-count":3,"title":["Triplet Adaptation Framework for Robust Semi-Supervised Learning"],"prefix":"10.1109","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2480-6538","authenticated-orcid":false,"given":"Ruibing","family":"Hou","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Information Processing, Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS), Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2668-0070","authenticated-orcid":false,"given":"Hong","family":"Chang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Information Processing, Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS), Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8984-205X","authenticated-orcid":false,"given":"Bingpeng","family":"Ma","sequence":"additional","affiliation":[{"name":"University of the Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8348-392X","authenticated-orcid":false,"given":"Shiguang","family":"Shan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Information Processing, Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS), Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3024-4404","authenticated-orcid":false,"given":"Xilin","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Information Processing, Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS), Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207304"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7503.003.0022"},{"key":"ref4","article-title":"ReMixMatch: Semi-supervised learning with distribution alignment and augmentation anchoring","author":"Berthelot","year":"2019"},{"key":"ref5","first-page":"5049","article-title":"MixMatch: A holistic approach to semi-supervised learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Berthelot"},{"key":"ref6","first-page":"1567","article-title":"Learning imbalanced datasets with label-distribution-aware margin loss","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Cao"},{"key":"ref7","article-title":"Softmatch: Addressing the quantity-quality trade-off in semi-supervised learning","author":"Chen","year":"2023"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5763"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00949"},{"key":"ref10","article-title":"Cluster-aware semi-supervised learning: Relational knowledge distillation provably learns clustering","author":"Dong","year":"2024"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20056-4_31"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01417"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00311"},{"key":"ref14","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Finn"},{"key":"ref15","first-page":"8082","article-title":"Class-imbalanced semi-supervised learning with adaptive thresholding","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Guo"},{"key":"ref16","article-title":"Robust deep semi-supervised learning: A brief introduction","author":"Guo","year":"2022"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00550"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref19","article-title":"Benchmarking neural network robustness to common corruptions and perturbations","author":"Hendrycks","year":"2019"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00656"},{"key":"ref21","article-title":"On non-random missing labels in semi-supervised learning","author":"Hu","year":"2022"},{"key":"ref22","article-title":"Flatmatch: Bridging labeled data and unlabeled data with cross-sharpness for semi-supervised learning","author":"Huang","year":"2024"},{"key":"ref23","first-page":"26714","article-title":"Universal semi-supervised learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Huang"},{"key":"ref24","article-title":"Decoupling representation and classifier for long-tailed recognition","author":"Kang","year":"2019"},{"key":"ref25","first-page":"14567","article-title":"Distribution aligning refinery of pseudo-label for imbalanced semi-supervised learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Kim"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-016-0981-7"},{"key":"ref27","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00454"},{"key":"ref29","first-page":"11828","article-title":"Smoothed adaptive weighting for imbalanced semi-supervised learning: Improve reliability against unknown distribution data","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Lai"},{"key":"ref30","article-title":"Temporal ensembling for semi-supervised learning","author":"Laine","year":"2016"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00560"},{"key":"ref32","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. Workshop","author":"Lee"},{"key":"ref33","first-page":"7082","article-title":"ABC: Auxiliary balanced classifier for class-imbalanced semi-supervised learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lee"},{"key":"ref34","first-page":"63","article-title":"Characterizing the impacts of semi-supervised learning for weak supervision","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Li"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00934"},{"key":"ref36","article-title":"Instant: Semi-supervised learning with instance-dependent thresholds","author":"Li","year":"2024"},{"key":"ref37","first-page":"161","article-title":"Generalized semi-supervised learning via self-supervised feature adaptation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Liang"},{"key":"ref38","first-page":"97","article-title":"Learning transferable features with deep adaptation networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Long"},{"key":"ref39","first-page":"1647","article-title":"Conditional adversarial domain adaptation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Long"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.183"},{"key":"ref41","first-page":"2208","article-title":"Deep transfer learning with joint adaptation networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Long"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01120"},{"key":"ref43","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/BF00114804","article-title":"On learning sets and functions","volume":"4","author":"BalasNatarajan","year":"1989","journal-title":"Mach. Learn."},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00956"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"ref46","article-title":"OpenMatch: Open-set consistency regularization for semi-supervised learning with outliers","author":"Saito","year":"2021"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.5555\/3294996.3295163"},{"key":"ref48","first-page":"596","article-title":"FixMatch: Simplifying semi-supervised learning with consistency and confidence","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Sohn"},{"key":"ref49","article-title":"The semi-supervised inaturalist-aves challenge at FGVC7 workshop","author":"Su","year":"2021"},{"key":"ref50","first-page":"1195","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Tarvainen"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-019-05855-6"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00914"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/72.788640"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.572"},{"key":"ref56","article-title":"Revisiting deep semi-supervised learning: An empirical distribution alignment framework and its generalization bound","author":"Wang","year":"2022"},{"key":"ref57","article-title":"Imbalanced semi-supervised learning with bias adaptive classifier","author":"Wang","year":"2022"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01424"},{"key":"ref59","article-title":"FreeMatch: Self-adaptive thresholding for semi-supervised learning","author":"Wang","year":"2022"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01071"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00338"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01513"},{"key":"ref63","first-page":"6256","article-title":"Unsupervised data augmentation for consistency training","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Xie"},{"key":"ref64","first-page":"26523","article-title":"On the algorithmic stability of adversarial training","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Xing"},{"key":"ref65","first-page":"11525","article-title":"Dash: Semi-supervised learning with dynamic thresholding","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xu"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2019.2953375"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.5244\/C.30.87"},{"key":"ref68","first-page":"18408","article-title":"FlexMatch: Boosting semi-supervised learning with curriculum pseudo labeling","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-7687-1_79"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00953"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00974"},{"key":"ref72","first-page":"29168","article-title":"STEP: Out-of-distribution detection in the presence of limited in-distribution labeled data","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhou"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/34\/10746266\/10542567.pdf?arnumber=10542567","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:23:37Z","timestamp":1732667017000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10542567\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":72,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2024.3404450","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":[[2024,12]]}}}