{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T12:28:56Z","timestamp":1774355336514,"version":"3.50.1"},"reference-count":64,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Excellent Youth Program of State Key Laboratory of Multimodal Artificial Intelligence Systems","award":["MAIS2024311"],"award-info":[{"award-number":["MAIS2024311"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62201572"],"award-info":[{"award-number":["62201572"]}],"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":["61831022"],"award-info":[{"award-number":["61831022"]}],"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":["62276259"],"award-info":[{"award-number":["62276259"]}],"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":["U21B2010"],"award-info":[{"award-number":["U21B2010"]}],"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":["U2436210"],"award-info":[{"award-number":["U2436210"]}],"id":[{"id":"10.13039\/501100001809","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":[[2026,2]]},"DOI":"10.1109\/tpami.2025.3620388","type":"journal-article","created":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T17:40:39Z","timestamp":1760377239000},"page":"1932-1948","source":"Crossref","is-referenced-by-count":2,"title":["IRNet: Iterative Refinement Network for Noisy Partial Label Learning"],"prefix":"10.1109","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9477-0599","authenticated-orcid":false,"given":"Zheng","family":"Lian","sequence":"first","affiliation":[{"name":"National Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingyu","family":"Xu","sequence":"additional","affiliation":[{"name":"Seed Group of ByteDance, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lan","family":"Chen","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Multi-modal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7944-3458","authenticated-orcid":false,"given":"Licai","family":"Sun","sequence":"additional","affiliation":[{"name":"University of Oulu, Oulu, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1529-1552","authenticated-orcid":false,"given":"Bin","family":"Liu","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Multi-modal Artificial Intelligence Systems, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2839-5799","authenticated-orcid":false,"given":"Lei","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9344-6428","authenticated-orcid":false,"given":"Jianhua","family":"Tao","sequence":"additional","affiliation":[{"name":"Department of Automation, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6132"},{"key":"ref2","first-page":"1","article-title":"Exploiting class activation value for partial-label learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhang"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2014.2359642"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2723401"},{"key":"ref5","first-page":"1629","article-title":"Learnability of the superset label learning problem","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Liu"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-23525-7_16"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1093\/nsr\/nwx106"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/1460096.1460104"},{"key":"ref9","first-page":"548","article-title":"A conditional multinomial mixture model for superset label learning","volume-title":"Proc. 25th Int. Conf. Neural Inf. Process. Syst.","author":"Liu"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339616"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143865"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/291"},{"key":"ref13","first-page":"27119","article-title":"Instance-dependent partial label learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Xu"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206667"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.3233\/IDA-2006-10503"},{"key":"ref16","first-page":"24212","article-title":"Revisiting consistency regularization for deep partial label learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wu"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/502"},{"key":"ref18","first-page":"921","article-title":"Learning with multiple labels","volume-title":"Proc. 15th Int. Conf. Neural Inf. Process. Syst.","author":"Jin"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539363"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401958"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-016-5606-4"},{"key":"ref22","first-page":"1565","article-title":"Proper losses for learning from partial labels","volume-title":"Proc. 25th Int. Conf. Neural Inf. Process. Syst.","author":"Cid-Sueiro"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3275249"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-019-05855-6"},{"key":"ref25","first-page":"1501","article-title":"Learning from partial labels","volume":"12","author":"Cour","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10775"},{"key":"ref27","first-page":"4048","article-title":"Solving the partial label learning problem: An instance-based approach","volume-title":"Proc. 24th Int. Conf. Artif. Intell.","author":"Zhang"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939788"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2017.2669639"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6959"},{"key":"ref31","first-page":"6500","article-title":"Progressive identification of true labels for partial-label learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Lv"},{"key":"ref32","first-page":"10948","article-title":"Provably consistent partial-label learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Feng"},{"key":"ref33","first-page":"11091","article-title":"Leveraged weighted loss for partial label learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wen"},{"key":"ref34","first-page":"1","article-title":"PiCO: Contrastive label disambiguation for partial label learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Wang"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3342650"},{"key":"ref36","article-title":"A survey of label-noise representation learning: Past, present and future","author":"Han","year":"2020"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3152527"},{"key":"ref38","first-page":"20331","article-title":"Early-learning regularization prevents memorization of noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Liu"},{"key":"ref39","first-page":"8792","article-title":"Generalized cross entropy loss for training deep neural networks with noisy labels","volume-title":"Proc. 32nd Int. Conf. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00041"},{"key":"ref41","first-page":"2304","article-title":"MentorNet: Learning data-driven curriculum for very deep neural networks on corrupted labels","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Jiang"},{"key":"ref42","first-page":"1","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":"ref43","doi-asserted-by":"publisher","DOI":"10.4135\/9781071810118"},{"key":"ref44","first-page":"7164","article-title":"How does disagreement help generalization against label corruption","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yu"},{"key":"ref45","first-page":"1","article-title":"DivideMix: Learning with noisy labels as semi-supervised learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li"},{"key":"ref46","first-page":"1","article-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Hendrycks"},{"key":"ref47","first-page":"1","article-title":"Enhancing the reliability of out-of-distribution image detection in neural networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Liang"},{"key":"ref48","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref49","volume-title":"Pattern Classification","author":"Hart","year":"2000"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015557"},{"key":"ref51","first-page":"312","article-title":"Unsupervised label noise modeling and loss correction","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Arazo"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2005.10.028"},{"key":"ref53","first-page":"1","article-title":"Learning with feature-dependent label noise: A progressive approach","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhang"},{"key":"ref54","article-title":"Progressive purification for instance-dependent partial label learning","author":"Xu","year":"2022"},{"key":"ref55","article-title":"Deep learning for classical Japanese literature","author":"Clanuwat","year":"2018"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.277"},{"key":"ref57","first-page":"3072","article-title":"Learning with multiple complementary labels","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Feng"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10894"},{"key":"ref59","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. 33 rd Int. Conf. Neural Inf. Process. Syst.","author":"Paszke"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2933837"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3120012"},{"key":"ref62","first-page":"18661","article-title":"Supervised contrastive learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Khosla"},{"key":"ref63","article-title":"RandAugment: Practical data augmentation with no separate search","author":"Cubuk","year":"2019"},{"key":"ref64","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/34\/11345188\/11202439.pdf?arnumber=11202439","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T22:01:05Z","timestamp":1768255265000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11202439\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":64,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2025.3620388","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":[[2026,2]]}}}