{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T17:01:10Z","timestamp":1783530070143,"version":"3.55.0"},"reference-count":65,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"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":["2018AAA0102000"],"award-info":[{"award-number":["2018AAA0102000"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21B2038"],"award-info":[{"award-number":["U21B2038"]}],"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":["61931008"],"award-info":[{"award-number":["61931008"]}],"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":["62025604"],"award-info":[{"award-number":["62025604"]}],"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":["U1936208"],"award-info":[{"award-number":["U1936208"]}],"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":["6212200758"],"award-info":[{"award-number":["6212200758"]}],"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":["61976202"],"award-info":[{"award-number":["61976202"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004739","name":"Youth Innovation Promotion Association of the Chinese Academy of Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004739","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Strategic Priority Research Program of Chinese Academy of Sciences","award":["XDB28000000"],"award-info":[{"award-number":["XDB28000000"]}]},{"name":"China National Postdoctoral Program for Innovative Talents","award":["BX2021298"],"award-info":[{"award-number":["BX2021298"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2022M713101"],"award-info":[{"award-number":["2022M713101"]}],"id":[{"id":"10.13039\/501100002858","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":[[2023,5,1]]},"DOI":"10.1109\/tpami.2022.3208419","type":"journal-article","created":{"date-parts":[[2022,9,21]],"date-time":"2022-09-21T19:31:45Z","timestamp":1663788705000},"page":"5970-5987","source":"Crossref","is-referenced-by-count":33,"title":["MaxMatch: Semi-Supervised Learning With Worst-Case Consistency"],"prefix":"10.1109","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0148-8306","authenticated-orcid":false,"given":"Yangbangyan","family":"Jiang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodan","family":"Li","sequence":"additional","affiliation":[{"name":"Security Department of Alibaba Group, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuefeng","family":"Chen","sequence":"additional","affiliation":[{"name":"Security Department of Alibaba Group, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6885-1341","authenticated-orcid":false,"given":"Yuan","family":"He","sequence":"additional","affiliation":[{"name":"Security Department of Alibaba Group, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3512-7277","authenticated-orcid":false,"given":"Qianqian","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4409-4999","authenticated-orcid":false,"given":"Zhiyong","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7141-708X","authenticated-orcid":false,"given":"Xiaochun","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Technology, Shenzhen Campus, Sun Yat-sen University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7542-296X","authenticated-orcid":false,"given":"Qingming","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref3","first-page":"596","article-title":"FixMatch: Simplifying semi-supervised learning with consistency\n      and confidence","volume-title":"Proc. Int. Conf. Neural Inf. Process.\n      Syst.","author":"Kurakin"},{"key":"ref4","first-page":"10 758","article-title":"Consistency-based semi-supervised learning for object\n      detection","volume-title":"Proc. Int. Conf. Neural Inf. Process.\n      Syst.","author":"Jeong"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2960224"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00451"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01332"},{"key":"ref8","first-page":"1195","article-title":"Mean teachers\n      are better role models: Weight-averaged consistency targets improve semi-supervised deep\n      learning results","volume-title":"Proc. Int. Conf. Neural Inf. Process.\n      Syst.","author":"Tarvainen"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858821"},{"key":"ref10","first-page":"5050","article-title":"MixMatch: A\n      holistic approach to semi-supervised learning","volume-title":"Proc. Int.\n      Conf. Neural Inf. Process. Syst.","author":"Berthelot"},{"key":"ref11","first-page":"6256","article-title":"Unsupervised data\n      augmentation for consistency training","volume-title":"Proc. Int. Conf.\n      Neural Inf. Process. Syst.","author":"Xie"},{"key":"ref12","first-page":"1","article-title":"ReMixMatch: Semi-supervised learning with distribution matching\n      and augmentation anchoring","volume-title":"Proc. Int. Conf. Learn.\n      Representations","author":"Berthelot"},{"key":"ref13","first-page":"1","article-title":"Temporal\n      ensembling for semi-supervised learning","volume-title":"Proc. Int. Conf.\n      Learn. Representations","author":"Laine"},{"key":"ref14","first-page":"1","article-title":"Theoretical\n      analysis of self-training with deep networks on unlabeled data","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Wei"},{"key":"ref15","article-title":"Adversarially robust generalization just requires more\n      unlabeled data","author":"Zhai","year":"2019"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-019-05855-6"},{"key":"ref17","first-page":"57","article-title":"Semi-supervised\n      classification by low density separation","volume-title":"Proc. Int.\n      Workshop Artif. Intell. Statist.","author":"Chapelle"},{"key":"ref18","first-page":"1","article-title":"Pseudo-label: The\n      simple and efficient semi-supervised learning method for deep neural\n     networks","volume-title":"Proc. Int. Conf. Mach. Learn. Workshop","author":"Lee"},{"key":"ref19","first-page":"3546","article-title":"Semi-supervised\n      learning with ladder networks","volume-title":"Proc. Int. Conf. Neural Inf.\n      Process.","author":"Rasmus"},{"key":"ref20","first-page":"1163","article-title":"Regularization with stochastic transformations and perturbations for\n      deep semi-supervised learning","volume-title":"Proc. Int. Conf. Neural Inf.\n      Process.","author":"Sajjadi"},{"key":"ref21","first-page":"529","article-title":"Semi-supervised learning by entropy\n     minimization","volume-title":"Proc. Int. Conf. Neural Inf. Process.\n      Syst.","author":"Grandvalet"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/504"},{"key":"ref23","first-page":"1","article-title":"Mixup:\n      Beyond empirical risk minimization","volume-title":"Proc. Int. Conf. Learn.\n      Representations","author":"Zhang"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"ref25","first-page":"21 786","article-title":"Not all\n      unlabeled data are equal: Learning to weight data in semi-supervised\n     learning","volume-title":"Proc. Int. Conf. Neural Inf. Process.\n      Syst.","author":"Ren"},{"key":"ref26","first-page":"22 243","article-title":"Big\n      self-supervised models are strong semi-supervised learners","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Chen"},{"key":"ref27","first-page":"1476","article-title":"S4L:\n      Self-supervised semi-supervised learning","volume-title":"Proc. IEEE\/CVF Int. Conf. Comput. Vis.","author":"Beyer"},{"key":"ref28","first-page":"1704","article-title":"Negative\n      sampling in semi-supervised learning","volume-title":"Proc. Int. Conf.\n      Mach. Learn.","author":"Chen"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00397"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.3115\/981658.981684"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ACVMOT.2005.107"},{"key":"ref32","first-page":"616","article-title":"Self-training for enhancement and domain adaptation of statistical\n      parsers trained on small datasets","volume-title":"Proc. Annu. Meeting\n      Assoc. Comput. Linguistics","author":"Reichart"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01070"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00521"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01341"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00921"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i8.16852"},{"key":"ref38","first-page":"9972","article-title":"Unsupervised\n      semantic aggregation and deformable template matching for semi-supervised\n      learning","volume-title":"Proc. Int. Conf. Neural Inf. Process.\n      Syst.","author":"Han"},{"key":"ref39","first-page":"11 525","article-title":"Dash: Semi-supervised learning with dynamic\n      thresholding","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xu"},{"key":"ref40","first-page":"10 065","article-title":"Sinkhorn\n      label allocation: Semi-supervised classification via annealed\n     self-training","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Tai"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01347"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01139"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-396502-8.00022-X"},{"key":"ref44","volume-title":"Foundations of Machine\n      Learning","author":"Mohri","year":"2012"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1093\/imaiai\/iaz007"},{"key":"ref46","first-page":"9722","article-title":"Data-dependent\n      sample complexity of deep neural networks via Lipschitz augmentation","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Wei"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"ref48","first-page":"214","article-title":"Wasserstein\n      generative adversarial networks","volume-title":"Proc. Int. Conf. Mach.\n      Learn.","author":"Arjovsky"},{"key":"ref49","first-page":"224","article-title":"Generalization\n      and equilibrium in generative adversarial nets (GANs)","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Arora"},{"key":"ref50","volume-title":"An Introduction to Game Theory","volume":"3","author":"Osborne","year":"2004"},{"key":"ref51","first-page":"4880","article-title":"What is local\n      optimality in nonconvex-nonconcave minimax optimization?","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Jin"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1080\/10556788.2021.1895152"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804441"},{"key":"ref54","article-title":"Learning\n      multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.2118\/18761-MS"},{"key":"ref56","first-page":"215","article-title":"An analysis of\n      single-layer networks in unsupervised feature learning","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Coates"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.5244\/C.30.87"},{"issue":"86","key":"ref58","first-page":"2579","article-title":"Visualizing\n      data using t-SNE","volume":"9","author":"van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref59","article-title":"Improved baselines\n      with momentum contrastive learning","author":"Chen","year":"2020"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00026"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-8853-9"},{"key":"ref63","first-page":"1","article-title":"Generalization\n      bounds for deep convolutional neural networks","volume-title":"Proc. Int.\n      Conf. Learn. Representations","author":"Long"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2021.3101125"},{"key":"ref65","first-page":"8030","article-title":"Optimistic bounds for multi-output learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Reeve"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/10091695\/09897005.pdf?arnumber=9897005","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T17:50:18Z","timestamp":1726077018000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9897005\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,1]]},"references-count":65,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2022.3208419","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":[[2023,5,1]]}}}