{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T10:11:03Z","timestamp":1784110263016,"version":"3.55.0"},"reference-count":74,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020AAA0106300"],"award-info":[{"award-number":["2020AAA0106300"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC) project","doi-asserted-by":"publisher","award":["62076196"],"award-info":[{"award-number":["62076196"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC) project","doi-asserted-by":"publisher","award":["11690011"],"award-info":[{"award-number":["11690011"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC) project","doi-asserted-by":"publisher","award":["61721002"],"award-info":[{"award-number":["61721002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC) project","doi-asserted-by":"publisher","award":["U1811461"],"award-info":[{"award-number":["U1811461"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006469","name":"Macao Science and Technology Development Fund","doi-asserted-by":"publisher","award":["061\/2020\/A2"],"award-info":[{"award-number":["061\/2020\/A2"]}],"id":[{"id":"10.13039\/501100006469","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1109\/tnnls.2021.3105104","type":"journal-article","created":{"date-parts":[[2021,8,30]],"date-time":"2021-08-30T20:53:40Z","timestamp":1630356820000},"page":"1194-1208","source":"Crossref","is-referenced-by-count":14,"title":["A Probabilistic Formulation for Meta-Weight-Net"],"prefix":"10.1109","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9956-0064","authenticated-orcid":false,"given":"Qian","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9968-3048","authenticated-orcid":false,"given":"Jun","family":"Shu","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziming","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1294-8283","authenticated-orcid":false,"given":"Deyu","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"4334","article-title":"Learning to reweight examples for robust deep learning","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Ren"},{"key":"ref2","first-page":"1917","article-title":"Meta-Weight-Net: Learning an explicit mapping for sample weighting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Shu"},{"key":"ref3","first-page":"1","article-title":"Understanding deep learning requires rethinking generalization","volume-title":"Proc. 5th Int. Conf. Learn. Represent.","author":"Zhang"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2008.239"},{"key":"ref5","first-page":"82","article-title":"Learning to predict from crowdsourced data","volume-title":"Proc. 30th Conf. Uncertainty Artif. Intell.","author":"Bi"},{"key":"ref6","first-page":"1746","article-title":"Learning to detect concepts from webly-labeled video data","volume-title":"Proc. 25th Int. Joint Conf. Artif. Intell.","author":"Liang"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.311"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2001.990517"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126229"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858826"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CIBCB48159.2020.9277638"},{"key":"ref13","first-page":"5947","article-title":"Exploring generalization in deep learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Neyshabur"},{"key":"ref14","first-page":"233","article-title":"A closer look at memorization in deep networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Arpit"},{"key":"ref15","first-page":"1","article-title":"Sensitivity and generalization in neural networks: An empirical study","volume-title":"Proc. 6th Int. Conf. Learn. Represent.","author":"Novak"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2011.2161285"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2018.07.011"},{"key":"ref18","first-page":"2304","article-title":"MentorNet: Learning data-driven curriculum for very deep neural networks on corrupted labels","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Jiang"},{"key":"ref19","first-page":"1189","article-title":"Self-paced learning for latent variable models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Kumar"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654918"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2017.05.043"},{"issue":"1","key":"ref22","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1023\/A:1023709501986","article-title":"A framework for robust subspace learning","volume":"54","author":"De la Torre","year":"2003","journal-title":"Int. J. Comput. Vis."},{"key":"ref23","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":"ref24","doi-asserted-by":"publisher","DOI":"10.1006\/jcss.1997.1504"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1016218223"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298885"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-30164-8_694"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/34.709601"},{"issue":"1","key":"ref29","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":"ref30","doi-asserted-by":"publisher","DOI":"10.1002\/eap.2043"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.205"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2832629"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015425"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.5555\/1642194.1642224"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2732482"},{"key":"ref36","first-page":"2078","article-title":"Self-paced learning with diversity","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Jiang"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v29i1.9608"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0192-5"},{"key":"ref39","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Finn"},{"key":"ref40","first-page":"1","article-title":"Optimization as a model for few-shot learning","volume-title":"Proc. 5th Int. Conf. Learn. Represent.","author":"Ravi"},{"key":"ref41","first-page":"4077","article-title":"Prototypical networks for few-shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Snell"},{"key":"ref42","first-page":"1","article-title":"Fidelity-weighted learning","volume-title":"Proc. 6th Int. Conf. Learn. Represent.","author":"Dehghani"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.4324\/9780203416013_chapter_1"},{"key":"ref44","first-page":"6466","article-title":"Learning to teach with dynamic loss functions","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wu"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1002\/SERIES1345"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/218380.218498"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/S0378-3758(00)00115-4"},{"key":"ref48","first-page":"985","article-title":"Covariate shift adaptation by importance weighted cross validation","volume":"8","author":"Sugiyama","year":"2007","journal-title":"J. Mach. Learn. Res."},{"key":"ref49","first-page":"631","article-title":"Robust learning under uncertain test distributions: Relating covariate shift to model misspecification","volume-title":"Proc. 31st Int. Conf. Mach. Learn.","author":"Wen"},{"key":"ref50","first-page":"3646","article-title":"Robust probabilistic modeling with Bayesian data reweighting","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Wang"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00041"},{"key":"ref52","article-title":"Robust bi-tempered logistic loss based on Bregman divergences","volume-title":"Advances in Neural Information Processing Systems","author":"Amid","year":"2019"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.240"},{"key":"ref54","first-page":"10456","article-title":"Using trusted data to train deep networks on labels corrupted by severe noise","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hendrycks"},{"key":"ref55","first-page":"1","article-title":"Training deep neural networks on noisy labels with bootstrapping","volume-title":"Proc. 3rd Int. Conf. Learn. Represent. (ICLR)","author":"Reed"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00582"},{"key":"ref57","first-page":"1","article-title":"Self-tuning networks: Bilevel optimization of hyperparameters using structured best-response functions","volume-title":"Proc. 7th Int. Conf. Learn. Represent.","author":"MacKay"},{"key":"ref58","first-page":"17649","article-title":"Learning to mutate with hypergradient guided population","volume-title":"Advances in Neural Information Processing Systems","author":"Tao","year":"2020"},{"key":"ref59","article-title":"An overview of deep learning architectures in few-shot learning domain","volume-title":"arXiv:2008.06365","author":"Jadon","year":"2020"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-5619-7_25"},{"key":"ref61","first-page":"1","article-title":"Auto-encoding variational Bayes","volume-title":"Proc. 2nd Int. Conf. Learn. Represent.","author":"Kingma"},{"key":"ref62","first-page":"2235","article-title":"Pathwise derivatives beyond the reparameterization trick","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Jankowiak"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3455008"},{"key":"ref64","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. 3rd Int. Conf. Learn. Represent.","author":"Kingma"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00906"},{"key":"ref66","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00949"},{"key":"ref69","first-page":"6222","article-title":"L_DMI: A novel information-theoretic loss function for training deep nets robust to label noise","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Xu"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33019103"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00519"},{"key":"ref72","first-page":"6256","article-title":"Unsupervised data augmentation for consistency training","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Xie"},{"key":"ref73","first-page":"813","article-title":"Variational inference based on robust divergences","volume-title":"Proc. 21st Int. Conf. Artif. Intell. Statist.","volume":"84","author":"Futami"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553380"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10056374\/09525050.pdf?arnumber=9525050","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T23:02:49Z","timestamp":1705014169000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9525050\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3]]},"references-count":74,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2021.3105104","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3]]}}}