{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T18:32:39Z","timestamp":1777487559031,"version":"3.51.4"},"reference-count":50,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["20H04206"],"award-info":[{"award-number":["20H04206"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"name":"RGC Early Career Scheme","award":["22200720"],"award-info":[{"award-number":["22200720"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61973162"],"award-info":[{"award-number":["61973162"]}],"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":["62006202"],"award-info":[{"award-number":["62006202"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013314","name":"Higher Education Discipline Innovation Project","doi-asserted-by":"publisher","award":["B13022"],"award-info":[{"award-number":["B13022"]}],"id":[{"id":"10.13039\/501100013314","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":[[2022]]},"DOI":"10.1109\/tpami.2022.3178690","type":"journal-article","created":{"date-parts":[[2022,5,30]],"date-time":"2022-05-30T22:23:56Z","timestamp":1653949436000},"page":"1-1","source":"Crossref","is-referenced-by-count":11,"title":["Class-Wise Denoising for Robust Learning under Label Noise"],"prefix":"10.1109","author":[{"given":"Chen","family":"Gong","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, the Jiangsu Key Laboratory of Image and Video Understanding for Social Security, and School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, P.R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongliang","family":"Ding","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, the Jiangsu Key Laboratory of Image and Video Understanding for Social Security, and School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, P.R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hong Kong Baptist University, Hong Kong, P.R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Niu","sequence":"additional","affiliation":[{"name":"RIKEN Center for Advanced Intelligence Project, Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer Science, Nankai University, Tianjin, P.R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jane J.","family":"You","sequence":"additional","affiliation":[{"name":"Department of Computing, Hong Kong Polytechnic University, Hong Kong, P.R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dacheng","family":"Tao","sequence":"additional","affiliation":[{"name":"JD Explore Academy, China and the University of Sydney, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masashi","family":"Sugiyama","sequence":"additional","affiliation":[{"name":"RIKEN Center for Advanced Intelligence Project, Tokyo, Japan; and is also with the Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","article-title":"A survey of label-noise representation learning: Past, present and future","author":"Han","year":"2020"},{"key":"ref2","first-page":"920","article-title":"Eliminating class noise in large datasets","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhu"},{"key":"ref3","first-page":"8527","article-title":"Co-teaching: Robust training of deep neural networks with extremely noisy labels","volume-title":"Proc. Adv. Neural Informat. Process. Syst.","author":"Han"},{"key":"ref4","first-page":"1","article-title":"Curriculum loss: Robust learning and generalization against label corruption","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Lyu"},{"key":"ref5","first-page":"1","article-title":"Dividemix: Learning with noisy labels as semi-supervised learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10894"},{"key":"ref7","first-page":"6543","article-title":"Normalized loss functions for deep learning with noisy labels","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ma"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00041"},{"key":"ref9","first-page":"1196","article-title":"Learning with noisy labels","volume-title":"Proc. Adv. Neural Informat. Process. Syst.","author":"Natarajan"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.240"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10293"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3044997"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1613\/jair.606"},{"key":"ref14","first-page":"233","article-title":"A closer look at memorization in deep networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Arpit"},{"key":"ref15","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":"ref16","first-page":"7164","article-title":"How does disagreement help generalization against label corruption?","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yu"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01374"},{"key":"ref18","first-page":"10 789","article-title":"Searching to exploit memorization effect in learning with noisy labels","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yao"},{"key":"ref19","first-page":"4006","article-title":"SIGUA: Forgetting may make learning with noisy labels more robust","volume-title":"Proc. Int. Conf. Mach. Learn..","author":"Han"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00718"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00582"},{"key":"ref22","first-page":"5601","article-title":"Toward robustness against label noise in training deep discriminative neural networks","volume-title":"Proc. 31st Int. Conf. Neural Informat. Process. Syst.","author":"Vahdat"},{"key":"ref23","first-page":"8778","article-title":"Generalized cross entropy loss for training deep neural networks with noisy labels","volume-title":"Proc. Adv. Neural Informat. Process. Syst.","author":"Zhang"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00536"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/305"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2456899"},{"key":"ref27","first-page":"6835","article-title":"Are anchor points really indispensable in label-noise learning?","volume-title":"Proc. Adv. Neural Informat. Process. Syst.","author":"Xia"},{"key":"ref28","first-page":"1","article-title":"Dual T: Reducing estimation error for transition matrix in label-noise learning","volume-title":"Proc. Adv. Neural Informat. Process. Syst.","author":"Yao"},{"key":"ref29","first-page":"6403","article-title":"Provably end-to-end label-noise learning without anchor points","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li"},{"key":"ref30","article-title":"Learning with confident examples: Rank pruning for robust classification with noisy labels","volume-title":"Proc. Conf. Uncertainty Artif. Intell.","author":"Northcutt"},{"key":"ref31","first-page":"838","article-title":"A rate of convergence for mixture proportion estimation, with application to learning from noisy labels","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Scott"},{"key":"ref32","first-page":"1","article-title":"Training deep neural-networks using a noise adaptation layer","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Goldberger"},{"key":"ref33","first-page":"1","article-title":"Parts-dependent label noise: Towards instance-dependent label noise","volume-title":"Proc. Adv. Neural Informat. Process. Syst.","author":"Xia"},{"issue":"1","key":"ref34","first-page":"5666","article-title":"Cost-sensitive learning with noisy labels","volume":"18","author":"Natarajan","year":"2017","journal-title":"J. Mach. Learn. Res."},{"key":"ref35","first-page":"708","article-title":"Loss factorization, weakly supervised learning and label noise robustness","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Patrini"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2941684"},{"key":"ref37","first-page":"3763","article-title":"Robust inference via generative classifiers for handling noisy labels","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Lee"},{"issue":"1","key":"ref38","first-page":"1929","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":"ref39","first-page":"6222","article-title":"$\\mathcal {L}_\\text{DMI}$LDMI: A novel information-theoretic loss function for training deep nets robust to label noise.","volume-title":"Proc. Adv. Neural Informat. Process. Syst.","author":"Xu"},{"key":"ref40","first-page":"1","article-title":"When optimizing f-divergence is robust with label noise","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Wei"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00014"},{"key":"ref42","article-title":"UCI machine learning repository","author":"Dua","year":"2017"},{"key":"ref43","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kingma"},{"key":"ref44","article-title":"High-performance neural networks for visual object classification","author":"Cire\u015fan","year":"2011"},{"key":"ref45","first-page":"1","article-title":"Simple and effective regularization methods for training on noisily labeled data with generalization guarantee","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Hu"},{"key":"ref46","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref48","first-page":"5907","article-title":"SELFIE: Refurbishing unclean samples for robust deep learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Song"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298885"},{"key":"ref50","first-page":"1","article-title":"SELF: Learning to filter noisy labels with self-ensembling","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Nguyen"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/4359286\/09784878.pdf?arnumber=9784878","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T03:27:23Z","timestamp":1706758043000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9784878\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":50,"URL":"https:\/\/doi.org\/10.1109\/tpami.2022.3178690","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":[[2022]]}}}