{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T18:38:30Z","timestamp":1784572710752,"version":"3.55.0"},"reference-count":73,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172075"],"award-info":[{"award-number":["62172075"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Opening Project of Guangdong Province Key Laboratory of Cyber-Physical System"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1109\/tnnls.2022.3202752","type":"journal-article","created":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T22:56:38Z","timestamp":1663887398000},"page":"5170-5182","source":"Crossref","is-referenced-by-count":10,"title":["A Convergence Path to Deep Learning on Noisy Labels"],"prefix":"10.1109","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9000-7262","authenticated-orcid":false,"given":"Defu","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ivor W.","family":"Tsang","sequence":"additional","affiliation":[{"name":"Centre for Frontier AI Research, Agency for Science, Technology and Research (A&#x002A;STAR), Fusionopolis, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5133-0320","authenticated-orcid":false,"given":"Guowu","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2335749"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2657781"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3015790"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2546956"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2021.3071924"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2019.2931491"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115000"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.106983"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.12.038"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00178"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3007787.3001163"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9533554"},{"key":"ref14","first-page":"1","article-title":"Training convolutional networks with noisy labels","volume-title":"Proc. 3rd Int. Conf. Learn. Represent.","author":"Sukhbaatar"},{"key":"ref15","first-page":"1","article-title":"Training deep neural-networks using a noise adaptation layer","volume-title":"Proc. 5th Int. Conf. Learn. Represent.","author":"Goldberger"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.240"},{"key":"ref17","first-page":"1929","article-title":"Unbiased risk estimators can mislead: A case study of learning with complementary labels","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chou"},{"key":"ref18","first-page":"10948","article-title":"Provably consistent partial-label learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"33","author":"Feng"},{"key":"ref19","first-page":"6500","article-title":"Progressive identification of true labels for partial-label learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Lv"},{"key":"ref20","first-page":"3072","article-title":"Learning with multiple complementary labels","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Feng"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2021.3073250"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10894"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2013.2292894"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3446776"},{"key":"ref25","first-page":"233","article-title":"A closer look at memorization in deep networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn. (ICML)","author":"Krueger"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00156"},{"key":"ref27","first-page":"5049","article-title":"Mixmatch: A holistic approach to semi-supervised learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Berthelot"},{"key":"ref28","first-page":"3546","article-title":"Semi-supervised learning with ladder networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Rasmus"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858821"},{"key":"ref30","first-page":"2998","article-title":"Semi-supervised classification based on classification from positive and unlabeled data","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","volume":"70","author":"Sakai"},{"key":"ref31","first-page":"5639","article-title":"Learning from complementary labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ishida"},{"key":"ref32","first-page":"2971","article-title":"Complementary-label learning for arbitrary losses and models","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Ishida"},{"key":"ref33","first-page":"1501","article-title":"Learning from partial labels","volume":"12","author":"Cour","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/398"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/291"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3027605"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2021.3072041"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2021.3070843"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2021.3071474"},{"key":"ref40","first-page":"1062","article-title":"Understanding and utilizing deep neural networks trained with noisy labels","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00519"},{"key":"ref42","article-title":"Learning to reweight examples for robust deep learning","author":"Ren","year":"2018","journal-title":"arXiv:1803.09050"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2014.09.081"},{"key":"ref44","first-page":"1","article-title":"Co-teaching: Robust training of deep neural networks with extremely noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Han"},{"key":"ref45","first-page":"7164","article-title":"How does disagreement help generalization against label corruption?","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Yu"},{"key":"ref46","first-page":"1","article-title":"Decoupling \u2018when to update\u2019 from \u2018how to update","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Malach"},{"key":"ref47","article-title":"Learning from noisy labels with deep neural networks","author":"Sukhbaatar","year":"2014","journal-title":"arXiv:1406.2080"},{"key":"ref48","first-page":"306","article-title":"Estimating a Kernel Fisher discriminant in the presence of label noise","volume-title":"Proc. ICML","volume":"1","author":"Lawrence"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33460-3_15"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0121"},{"key":"ref51","first-page":"10456","article-title":"Using trusted data to train deep networks on labels corrupted by severe noise","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hendrycks"},{"key":"ref52","first-page":"5836","article-title":"Masking: A new perspective of noisy supervision","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Han"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9414493"},{"key":"ref54","first-page":"10","article-title":"Learning with symmetric label noise: The importance of being unhinged","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"28","author":"van Rooyen"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1287\/opre.1100.0854"},{"key":"ref56","first-page":"8778","article-title":"Generalized cross entropy loss for training deep neural networks with noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/305"},{"key":"ref58","first-page":"6543","article-title":"Normalized loss functions for deep learning with noisy labels","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","volume":"119","author":"Ma"},{"key":"ref59","first-page":"5739","article-title":"Learning with bad training data via iterative trimmed loss minimization","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Shen"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.283"},{"key":"ref61","article-title":"Training deep neural networks on noisy labels with bootstrapping","author":"Reed","year":"2014","journal-title":"arXiv:1412.6596"},{"key":"ref62","first-page":"3355","article-title":"Dimensionality-driven learning with noisy labels","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Ma"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00906"},{"key":"ref64","first-page":"1","article-title":"Prestopping: How does early stopping help generalization against label noise?","volume-title":"Proc. ICLR","author":"Song"},{"key":"ref65","first-page":"4313","article-title":"Gradient descent with early stopping is provably robust to label noise for overparameterized neural networks","volume-title":"Proc. 23rd Int. Conf. Artif. Intell. Statist.","author":"Li"},{"key":"ref66","first-page":"17044","article-title":"Identifying mislabeled data using the area under the margin ranking","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Pleiss"},{"key":"ref67","first-page":"1","article-title":"An empirical study of example forgetting during deep neural network learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Toneva"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1198\/016214505000000907"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1214\/12-EJS699"},{"key":"ref70","volume-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref71","first-page":"1","article-title":"Can gradient clipping mitigate label noise?","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Menon"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00041"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10492491\/09899463.pdf?arnumber=9899463","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T19:40:15Z","timestamp":1712691615000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9899463\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4]]},"references-count":73,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2022.3202752","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4]]}}}