{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T03:32:27Z","timestamp":1778643147367,"version":"3.51.4"},"reference-count":64,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1109\/cvpr46437.2021.00021","type":"proceedings-article","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T17:56:02Z","timestamp":1635875762000},"page":"144-153","source":"Crossref","is-referenced-by-count":48,"title":["Faster Meta Update Strategy for Noise-Robust Deep Learning"],"prefix":"10.1109","author":[{"given":"Youjiang","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linchao","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref38","author":"pleiss","year":"2020","journal-title":"Identifying mislabeled data using the area under the margin ranking"},{"key":"ref33","article-title":"Decoupling “ when to update” from\" how to update","author":"malach","year":"2017","journal-title":"NeurIPS"},{"key":"ref32","article-title":"Exploring the limits of weakly supervised pretraining","author":"mahajan","year":"2018","journal-title":"ECCV"},{"key":"ref31","article-title":"Dimensionality-driven learning with noisy labels","author":"ma","year":"2018","journal-title":"ICML"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00264"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.240"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1.12125"},{"key":"ref35","article-title":"Adding gradient noise improves learning for very deep networks","author":"neelakantan","year":"2015"},{"key":"ref34","article-title":"Reducing reparameterization gradient variance","author":"miller","year":"2017","journal-title":"NeurIPS"},{"key":"ref60","article-title":"Understanding deep learning requires rethinking generalization","author":"zhang","year":"2017","journal-title":"ICLRE"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00931"},{"key":"ref61","article-title":"mixup: Beyond empirical risk minimization","author":"zhang","year":"2018","journal-title":"ICLRE"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00974"},{"key":"ref27","article-title":"Simaug: Learning robust representations from simulation for trajectory prediction","author":"liang","year":"2020","journal-title":"ECCV"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00440"},{"key":"ref29","article-title":"Ssd: Single shot multibox detector","author":"liu","year":"2016","journal-title":"ECCV"},{"key":"ref2","article-title":"Learning imbalanced datasets with label-distribution-aware margin loss","author":"cao","year":"2019","journal-title":"NeurIPS"},{"key":"ref1","article-title":"Unsupervised label noise modeling and loss correction","author":"arazo","year":"2019","journal-title":"ICML"},{"key":"ref20","article-title":"Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels","author":"jiang","year":"2018","journal-title":"ICML"},{"key":"ref22","author":"krizhevsky","year":"2009","journal-title":"Learning multiple layers of features from tiny images"},{"key":"ref21","first-page":"3573","article-title":"Cost-sensitive learning of deep feature representations from imbalanced data","volume":"29","author":"khan","year":"2017","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"ref24","article-title":"Dividemix: Learning with noisy labels as semi-supervised learning","author":"li","year":"0"},{"key":"ref23","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"NeurIPS"},{"key":"ref26","article-title":"Webvision database: Visual learning and understanding from web data","author":"li","year":"2017"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00519"},{"key":"ref50","author":"vyas","year":"2020","journal-title":"Learning soft labels via meta learning"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00458"},{"key":"ref59","article-title":"Wide residual networks","author":"zagoruyko","year":"2016","journal-title":"BMCV"},{"key":"ref58","article-title":"How does disagreement help generalization against label corruption?","author":"yu","year":"2019","journal-title":"ICML"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00585"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00718"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00297"},{"key":"ref54","article-title":"Learning from massive noisy labeled data for image classification","author":"xiao","year":"2015","journal-title":"CVPR"},{"key":"ref53","article-title":"Are anchor points really indispensable in label-noise learning?","author":"xia","year":"2019","journal-title":"NeurIPS"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01374"},{"key":"ref10","article-title":"Borderline-smote: a new over-sampling method in imbalanced data sets learning","author":"han","year":"2005","journal-title":"International Conference on Intelligent Computing"},{"key":"ref40","article-title":"Learning to reweight examples for robust deep learning","author":"ren","year":"2018","journal-title":"ICML"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2008.239"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref14","article-title":"Identity mappings in deep residual networks","author":"he","year":"2016","journal-title":"ECCV"},{"key":"ref15","article-title":"Using trusted data to train deep networks on labels corrupted by severe noise","author":"hendrycks","year":"2018","journal-title":"NeurIPS"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00763"},{"key":"ref17","author":"jang","year":"2017","journal-title":"Categorical repa-rameterization with gumbel-softmax"},{"key":"ref18","article-title":"Beyond synthetic noise: Deep learning on controlled noisy labels","author":"jiang","year":"2020","journal-title":"ICML"},{"key":"ref19","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v29i1.9608","article-title":"Self-paced curriculum learning","author":"jiang","year":"2015","journal-title":"AAAI"},{"key":"ref4","article-title":"Understanding and utilizing deep neural networks trained with noisy labels","author":"chen","year":"2019","journal-title":"ICML"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00949"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.529"},{"key":"ref8","article-title":"Training deep neural-networks using a noise adaptation layer","author":"goldberger","year":"2017","journal-title":"ICLRE"},{"key":"ref49","article-title":"Matching networks for one shot learning","author":"vinyals","year":"2016","journal-title":"NeurIPS"},{"key":"ref7","first-page":"1","article-title":"C4. 5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling","volume":"11","author":"drummond","year":"2003","journal-title":"Workshop on Learning from Imbalanced Datasets II ICML"},{"key":"ref9","article-title":"Co-teaching: Robust training of deep neural networks with extremely noisy labels","author":"han","year":"2018","journal-title":"NeurIPS"},{"key":"ref46","article-title":"Inception-v4, inception-resnet and the impact of residual connections on learning","author":"szegedy","year":"2016","journal-title":"AAAI"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.427"},{"key":"ref48","article-title":"Toward robustness against label noise in training deep discriminative neural networks","author":"vahdat","year":"2017","journal-title":"NeurIPS"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00582"},{"key":"ref42","article-title":"Relay back-propagation for effective learning of deep convolutional neural networks","author":"shen","year":"2016","journal-title":"ECCV"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"ref44","author":"shu","year":"2020","journal-title":"Meta transition adaptation for robust deep learning with noisy labels"},{"key":"ref43","article-title":"Meta-weight-net: Learning an explicit mapping for sample weighting","author":"shu","year":"2019","journal-title":"NeurIPS"}],"event":{"name":"2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","location":"Nashville, TN, USA","start":{"date-parts":[[2021,6,20]]},"end":{"date-parts":[[2021,6,25]]}},"container-title":["2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9577055\/9577056\/09578504.pdf?arnumber=9578504","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,12]],"date-time":"2023-11-12T00:07:16Z","timestamp":1699747636000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9578504\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6]]},"references-count":64,"URL":"https:\/\/doi.org\/10.1109\/cvpr46437.2021.00021","relation":{},"subject":[],"published":{"date-parts":[[2021,6]]}}}