{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T20:59:24Z","timestamp":1767646764650,"version":"3.48.0"},"reference-count":63,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"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":["62176097"],"award-info":[{"award-number":["62176097"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Hubei Provincial Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022CFA055"],"award-info":[{"award-number":["2022CFA055"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Circuits Syst. Video Technol."],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1109\/tcsvt.2024.3478771","type":"journal-article","created":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T13:25:11Z","timestamp":1728653111000},"page":"1648-1659","source":"Crossref","is-referenced-by-count":1,"title":["Linear Feature Source Prediction and Recombination Network for Noisy Label Learning"],"prefix":"10.1109","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1513-7291","authenticated-orcid":false,"given":"Ruochen","family":"Zheng","sequence":"first","affiliation":[{"name":"National Key Laboratory of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9403-353X","authenticated-orcid":false,"given":"Chuchu","family":"Han","sequence":"additional","affiliation":[{"name":"Baidu, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2736-3920","authenticated-orcid":false,"given":"Changxin","family":"Gao","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9167-1496","authenticated-orcid":false,"given":"Nong","family":"Sang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298885"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.4135\/9781071810118"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00582"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00718"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00041"},{"key":"ref6","first-page":"7597","article-title":"Part-dependent label noise: Towards instance-dependent label noise","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Xia"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00041"},{"key":"ref8","first-page":"24392","article-title":"Understanding and improving early stopping for learning with noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Bai"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/tcsvt.2022.3231887"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/tcsvt.2023.3286546"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00935"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.48550\/arxiv.1710.09412"},{"key":"ref13","first-page":"6438","article-title":"Manifold mixup: Better representations by interpolating hidden states","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Verma"},{"key":"ref14","first-page":"19365","article-title":"Self-adaptive training: Beyond empirical risk minimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Huang"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00342"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00571"},{"key":"ref17","article-title":"DivideMix: Learning with noisy labels as semi-supervised learning","author":"Li","year":"2020","journal-title":"arXiv:2002.07394"},{"key":"ref18","first-page":"312","article-title":"Unsupervised label noise modeling and loss correction","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Arazo"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00945"},{"key":"ref20","first-page":"1","article-title":"Mixmatch: A holistic approach to semi-supervised learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Berthelot"},{"key":"ref21","article-title":"MoPro: Webly supervised learning with momentum prototypes","author":"Li","year":"2020","journal-title":"arXiv:2009.07995"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref23","article-title":"Improved baselines with momentum contrastive learning","author":"Chen","year":"2020","journal-title":"arXiv:2003.04297"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525087"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3169145"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3221611"},{"key":"ref27","first-page":"1","article-title":"Unsupervised representation learning by predicting image rotations","volume-title":"Proc. ICLR","author":"Gidaris"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46487-9_40"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.278"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3114209"},{"key":"ref32","first-page":"1","article-title":"An image is worth 16\u00d716 words: Transformers for image recognition at scale","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Dosovitskiy"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3186751"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.12.100"},{"key":"ref35","first-page":"66","article-title":"Camera style and identity disentangling network for person re-identification","volume-title":"Proc. BMVC","author":"Zheng"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3240464"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"ref38","article-title":"REPAIR: Rank correlation and noisy pair half-replacing with memory for noisy correspondence","author":"Zheng","year":"2024","journal-title":"arXiv:2403.08224"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3109084"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3179441"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/3446776"},{"key":"ref42","first-page":"1","article-title":"A closer look at memorization in deep networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Arpit"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.240"},{"key":"ref44","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.","volume":"33","author":"Yao"},{"key":"ref45","first-page":"11285","article-title":"Class2simi: A noise reduction perspective on learning with noisy labels","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","volume":"139","author":"Wu"},{"key":"ref46","first-page":"1","article-title":"Are anchor points really indispensable in label-noise learning?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Xia"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58517-4_46"},{"key":"ref48","first-page":"20331","article-title":"Early-learning regularization prevents memorization of noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","volume":"33","author":"Liu"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00524"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00654"},{"article-title":"Learning multiple layers of features from tiny images","year":"2009","author":"Krizhevsky","key":"ref51"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref54","first-page":"7164","article-title":"How does disagreement help generalization against label corruption?","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yu"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01374"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00519"},{"key":"ref57","first-page":"1","article-title":"Meta-weight-net: Learning an explicit mapping for sample weighting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Shu"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17319"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01909"},{"key":"ref60","article-title":"Learning with noisy labels revisited: A study using real-world human annotations","author":"Wei","year":"2021","journal-title":"arXiv:2110.12088"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00932"},{"key":"ref62","article-title":"Training deep neural networks on noisy labels with bootstrapping","author":"Reed","year":"2014","journal-title":"arXiv:1412.6596"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01553"}],"container-title":["IEEE Transactions on Circuits and Systems for Video Technology"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/76\/10885778\/10714416.pdf?arnumber=10714416","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T18:41:37Z","timestamp":1767638497000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10714416\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2]]},"references-count":63,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tcsvt.2024.3478771","relation":{},"ISSN":["1051-8215","1558-2205"],"issn-type":[{"type":"print","value":"1051-8215"},{"type":"electronic","value":"1558-2205"}],"subject":[],"published":{"date-parts":[[2025,2]]}}}