{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T02:37:14Z","timestamp":1772159834419,"version":"3.50.1"},"reference-count":68,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"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":["62171123"],"award-info":[{"award-number":["62171123"]}],"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":["62176055"],"award-info":[{"award-number":["62176055"]}],"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":["62071241"],"award-info":[{"award-number":["62071241"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000288","name":"Royal Society","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000288","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62211530112"],"award-info":[{"award-number":["62211530112"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Program of China","award":["2023YFC3603600"],"award-info":[{"award-number":["2023YFC3603600"]}]},{"name":"National Key Research and Development Program of China","award":["2022YFC2405600"],"award-info":[{"award-number":["2022YFC2405600"]}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20192004"],"award-info":[{"award-number":["BK20192004"]}],"id":[{"id":"10.13039\/501100004608","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":[[2024,6]]},"DOI":"10.1109\/tpami.2024.3357518","type":"journal-article","created":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T15:47:39Z","timestamp":1706024859000},"page":"4460-4475","source":"Crossref","is-referenced-by-count":4,"title":["Student Loss: Towards the Probability Assumption in Inaccurate Supervision"],"prefix":"10.1109","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1572-6192","authenticated-orcid":false,"given":"Shuo","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Instrument Science and Engineering, the State Key Laboratory of Digital Medical Engineering, the School of Biological Science and Medical Engineering, Southeast University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3524-8933","authenticated-orcid":false,"given":"Jian-Qing","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5256-210X","authenticated-orcid":false,"given":"Hamido","family":"Fujita","sequence":"additional","affiliation":[{"name":"Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi, Kuala Lumpur, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3060-836X","authenticated-orcid":false,"given":"Yu-Wen","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6130-7220","authenticated-orcid":false,"given":"Deng-Bao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1552-5630","authenticated-orcid":false,"given":"Ting-Ting","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Engineering Science, University of Oxford, Oxford, U.K."}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1880-5918","authenticated-orcid":false,"given":"Min-Ling","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1965-3020","authenticated-orcid":false,"given":"Cheng-Yu","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Pervasive label errors in test sets destabilize machine learning benchmarks","author":"Northcutt","year":"2021"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1093\/nsr\/nwx106"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.168"},{"key":"ref4","first-page":"1","article-title":"Training convolutional networks with noisy labels","volume-title":"Proc. Int. Conf. Learn. Representations Worksheet","author":"Sukhbaatar"},{"issue":"1","key":"ref5","first-page":"44","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref6","first-page":"1","article-title":"Training deep neural-networks using a noise adaptation layer","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Goldberger"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2016.7472164"},{"key":"ref8","first-page":"5836","article-title":"Masking: A new perspective of noisy supervision","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Han"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298885"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2877939"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01249-6_38"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01150"},{"key":"ref13","first-page":"2712","article-title":"Using pre-training can improve model robustness and uncertainty","volume-title":"Proc. Proc. Int. Conf. Mach. Learn.","author":"Hendrycks"},{"key":"ref14","first-page":"1","article-title":"Robust early-learning: Hindering the memorization of noisy labels","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xia"},{"key":"ref15","first-page":"1","article-title":"Can gradient clipping mitigate label noise?","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Menon"},{"key":"ref16","first-page":"1","article-title":"Open-set label noise can improve robustness against inherent label noise","volume-title":"Proc Adv. Neural Inf. Process. Syst.","author":"Wei"},{"key":"ref17","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":"ref18","first-page":"960","article-title":"Decoupling \u2018when to update\u2019 from \u2018how to update\u2019","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Malach"},{"key":"ref19","first-page":"8527","article-title":"Co-teaching: Robust training of deep neural networks with extremely noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Han"},{"key":"ref20","first-page":"7164","article-title":"How does disagreement help generalization against label corruption?","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yu"},{"key":"ref21","first-page":"1062","article-title":"Understanding and utilizing deep neural networks trained with noisy labels","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01374"},{"key":"ref23","first-page":"1","article-title":"Dividemix: Learning with noisy labels as semi-supervised learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li"},{"key":"ref24","first-page":"5050","article-title":"MixMatch: A holistic approach to semi-supervised learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Berthelot"},{"key":"ref25","first-page":"1","article-title":"Robust curriculum learning: From clean label detection to noisy label self-correction","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhou"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10894"},{"key":"ref27","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":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00019"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00041"},{"key":"ref30","first-page":"6543","article-title":"Normalized loss functions for deep learning with noisy labels","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ma"},{"key":"ref31","first-page":"1","article-title":"Curriculum loss: Robust learning and generalization against label corruption","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Lyu"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00932"},{"key":"ref33","first-page":"30284","article-title":"Generalized Jensen-Shannon divergence loss for learning with noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Englesson"},{"key":"ref34","first-page":"12846","article-title":"Asymmetric loss functions for learning with noisy labels","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"139","author":"Zhou"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3236459"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.240"},{"key":"ref37","first-page":"1","article-title":"Are anchor points really indispensable in label-noise learning?","volume-title":"Proc. Adv. Neural Inf. Process. Syst","author":"Xia"},{"key":"ref38","first-page":"7260","article-title":"Dual T: Reducing estimation error for transition matrix in label-noise learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Yao"},{"key":"ref39","first-page":"1002","article-title":"Active bias: Training more accurate neural networks by emphasizing high variance samples","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Chang"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00953"},{"key":"ref41","first-page":"1","article-title":"Training deep neural networks on noisy labels with bootstrapping","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Reed"},{"key":"ref42","first-page":"3355","article-title":"Dimensionality-driven learning with noisy labels","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ma"},{"key":"ref43","first-page":"5907","article-title":"Selfie: Refurbishing unclean samples for robust deep learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Song"},{"key":"ref44","first-page":"1","article-title":"Introduction to the gamma function","author":"Sebah","year":"2002"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46478-7_31"},{"key":"ref46","first-page":"507","article-title":"Large-margin Softmax loss for convolutional neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Liu"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10243"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3166879"},{"key":"ref49","first-page":"20331","article-title":"Early-learning regularization prevents memorization of noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Liu"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/s00181-018-1570-0"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/j.spl.2018.05.014"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1239\/aap\/1118858629"},{"key":"ref53","article-title":"The MNIST database of handwritten digits","author":"LeCun","year":"1998"},{"key":"ref54","article-title":"CIFAR-10 and CIFAR-100 datasets","author":"Krizhevsky","year":"2014"},{"key":"ref55","article-title":"Web vision database: Visual learning and understanding from web data","author":"Li","year":"2017"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00044"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00302"},{"key":"ref59","first-page":"1","article-title":"Learning with feature-dependent label noise: A progressive approach","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhang"},{"key":"ref60","first-page":"1062","article-title":"Understanding and utilizing deep neural networks trained with noisy labels","volume-title":"Proc. AAAI Conf. Artif. Intell.","author":"Chen"},{"key":"ref61","first-page":"1","article-title":"Dividemix: Learning with noisy labels as semi-supervised learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li"},{"key":"ref62","first-page":"20331","article-title":"Early-learning regularization prevents memorization of noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst","author":"Liu"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00935"},{"key":"ref64","first-page":"1","article-title":"MoPro: Webly supervised learning with momentum prototypes","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00014"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00552"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00482"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/10522060\/10412669.pdf?arnumber=10412669","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T13:33:23Z","timestamp":1715175203000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10412669\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6]]},"references-count":68,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2024.3357518","relation":{"has-preprint":[{"id-type":"doi","id":"10.36227\/techrxiv.22258612.v1","asserted-by":"object"},{"id-type":"doi","id":"10.36227\/techrxiv.22258612","asserted-by":"object"}]},"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":[[2024,6]]}}}