{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T16:51:15Z","timestamp":1783788675745,"version":"3.55.0"},"reference-count":60,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"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":["62101032"],"award-info":[{"award-number":["62101032"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2021M690015"],"award-info":[{"award-number":["2021M690015"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2022T150050"],"award-info":[{"award-number":["2022T150050"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012236","name":"Beijing Institute of Technology Research Fund Program for Young Scholars","doi-asserted-by":"publisher","award":["3040011182111"],"award-info":[{"award-number":["3040011182111"]}],"id":[{"id":"10.13039\/501100012236","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":[[2023,12]]},"DOI":"10.1109\/tpami.2023.3311636","type":"journal-article","created":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T18:00:18Z","timestamp":1693850418000},"page":"14420-14434","source":"Crossref","is-referenced-by-count":13,"title":["Dynamic Loss for Robust Learning"],"prefix":"10.1109","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0914-4954","authenticated-orcid":false,"given":"Shenwang","family":"Jiang","sequence":"first","affiliation":[{"name":"Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6936-9485","authenticated-orcid":false,"given":"Jianan","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1015-0534","authenticated-orcid":false,"given":"Jizhou","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6807-1513","authenticated-orcid":false,"given":"Ying","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5452-2662","authenticated-orcid":false,"given":"Tingfa","family":"Xu","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","first-page":"1","article-title":"DivideMix: Learning with noisy labels as semi-supervised learning","author":"li","year":"2020","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-022-01643-3"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17319"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00021"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00076"},{"key":"ref14","first-page":"4175","article-title":"Balanced meta-softmax for long-tailed visual recognition","author":"ren","year":"2020","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref58","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref53","first-page":"1","article-title":"Long-tail learning via logit adjustment","author":"menon","year":"2021","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01622"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00264"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00075"},{"key":"ref10","first-page":"1","article-title":"Decoupling representation and classifier for long-tailed recognition","author":"kang","year":"2019","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00676"},{"key":"ref17","first-page":"1","article-title":"How to train your MAML","author":"antoniou","year":"2019","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i6.20654"},{"key":"ref19","first-page":"5907","article-title":"SELFIE: Refurbishing unclean samples for robust deep learning","author":"song","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref18","first-page":"1","article-title":"Learning multiple layers of features from tiny images","volume":"1","author":"krizhevsky","year":"2009"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00677"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00949"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3186930"},{"key":"ref45","first-page":"1","article-title":"Learning with feature-dependent label noise: A progressive approach","author":"zhang","year":"2021","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref48","first-page":"30284","article-title":"Generalized Jensen-Shannon divergence loss for learning with noisy labels","author":"englesson","year":"2021","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref47","first-page":"8536","article-title":"Co-teaching: Robust training of deep neural networks with extremely noisy labels","author":"han","year":"2018","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i12.26725"},{"key":"ref44","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","author":"radford","year":"2021","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref49","first-page":"630","article-title":"Identity mappings in deep residual networks","author":"he","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref8","first-page":"1","article-title":"Heteroskedastic and imbalanced deep learning with adaptive regularization","author":"cao","year":"2020","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i6.20661"},{"key":"ref9","first-page":"1919","article-title":"Meta-Weight-Net: Learning an explicit mapping for sample weighting","author":"shu","year":"2019","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref4","article-title":"Webvision database: Visual learning and understanding from web data","author":"li","year":"2017"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"ref6","first-page":"2304","article-title":"MentorNet: Learning data-driven curriculum for very deep neural networks on corrupted labels","author":"jiang","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref5","first-page":"1567","article-title":"Learning imbalanced datasets with label-distribution-aware margin loss","author":"cao","year":"2019","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref40","article-title":"Maximising the utility of validation sets for imbalanced noisy-label meta-learning","author":"hoang","year":"2022"},{"key":"ref35","first-page":"20331","article-title":"Early-learning regularization prevents memorization of noisy labels","author":"liu","year":"2020","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref34","article-title":"Robust long-tailed learning under label noise","author":"wei","year":"2021"},{"key":"ref37","first-page":"1","article-title":"SGDR: Stochastic gradient descent with warm restarts","author":"loshchilov","year":"2017","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00654"},{"key":"ref31","first-page":"1","article-title":"Training deep neural networks on noisy labels with bootstrapping","author":"reed","year":"2015","journal-title":"Proc Int Conf Learn Representations (Workshop)"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3263335"},{"key":"ref33","first-page":"5049","article-title":"MixMatch: A holistic approach to semi-supervised learning","author":"berthelot","year":"2019","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref32","article-title":"Sample prior guided robust model learning to suppress noisy labels","author":"chen","year":"2022"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref1","first-page":"211","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00463"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3271451"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.580"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2914680"},{"key":"ref25","first-page":"1","article-title":"Long-tail learning via logit adjustment","author":"menon","year":"2020","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"ref22","first-page":"1513","article-title":"Long-tailed classification by keeping the good and removing the bad momentum causal effect","author":"tang","year":"2020","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2018.07.011"},{"key":"ref28","first-page":"1189","article-title":"Self-paced learning for latent variable models","author":"kumar","year":"2010","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01168"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00342"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.5244\/C.30.87"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/10308548\/10238823.pdf?arnumber=10238823","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T19:52:27Z","timestamp":1701114747000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10238823\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12]]},"references-count":60,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2023.3311636","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":[[2023,12]]}}}