{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T22:21:38Z","timestamp":1787955698376,"version":"build-2784847793"},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"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":["61871052"],"award-info":[{"award-number":["61871052"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Affective Comput."],"published-print":{"date-parts":[[2023,7,1]]},"DOI":"10.1109\/taffc.2021.3131621","type":"journal-article","created":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T18:46:18Z","timestamp":1638297978000},"page":"2402-2414","source":"Crossref","is-referenced-by-count":32,"title":["Boosting Facial Expression Recognition by A Semi-Supervised Progressive Teacher"],"prefix":"10.1109","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5424-6338","authenticated-orcid":false,"given":"Jing","family":"Jiang","sequence":"first","affiliation":[{"name":"Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5952-6996","authenticated-orcid":false,"given":"Weihong","family":"Deng","sequence":"additional","affiliation":[{"name":"Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","article-title":"Omni-supervised facial expression recognition: A simple baseline","author":"liu","year":"2020"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2020.2973158"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/2818346.2830587"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2020.2981446"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICME.2019.00194"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2019.04.002"},{"key":"ref11","first-page":"1195","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","author":"tarvainen","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref55","article-title":"Learning confidence for out-of-distribution detection in neural networks","author":"devries","year":"2018"},{"key":"ref10","article-title":"Induced disgust, happiness and surprise: An addition to the MMI facial expression database","author":"valstar","year":"2010","journal-title":"Proc 3rd Int Workshop Emotion (Satellite of LREC) Corpora Res Emotion Affect"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2021.01.029"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2019.08.025"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_27"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-019-05855-6"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412493"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2018.00077"},{"key":"ref50","article-title":"Probabilistic attribute tree in convolutional neural networks for facial expression recognition","author":"cai","year":"2018"},{"key":"ref46","first-page":"233","article-title":"A closer look at memorization in deep networks","author":"arpit","year":"2017","journal-title":"Int Conf Mach Learn"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP40778.2020.9190643"},{"key":"ref48","article-title":"Learning face representation from scratch","author":"yi","year":"2014"},{"key":"ref47","first-page":"117","article-title":"Challenges in representation learning: A report on three machine learning contests","author":"goodfellow","year":"2013","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref42","first-page":"8527","article-title":"Co-teaching: Robust training of deep neural networks with extremely noisy labels","author":"han","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref41","first-page":"961","article-title":"Decoupling” when to update” from” how to update","author":"malach","year":"0","journal-title":"Proc 31st Int Conf Neural Inf Process Syst"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00800"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2018.2833032"},{"key":"ref49","article-title":"Fixmatch: Simplifying semi-supervised learning with consistency and confidence","volume":"33","author":"sohn","year":"2020","journal-title":"Adv Neural Inf Proc Syst"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2010.5543262"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/2993148.2993165"},{"key":"ref9","first-page":"1","article-title":"Facial expression recognition from near-infrared video sequences","author":"taini","year":"2008","journal-title":"Proc 19th Int Conf Pattern Recognit"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00693"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2868382"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2017.2740923"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.277"},{"key":"ref40","first-page":"1002","article-title":"Active bias: Training more accurate neural networks by emphasizing high variance samples","volume":"30","author":"chang","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2456899"},{"key":"ref34","first-page":"312","article-title":"Unsupervised label noise modeling and loss correction","author":"arazo","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref37","first-page":"1919","article-title":"Meta-weight-net: Learning an explicit mapping for sample weighting","volume":"32","author":"shu","year":"2019","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref36","first-page":"4334","article-title":"Learning to reweight examples for robust deep learning","author":"ren","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.696"},{"key":"ref30","first-page":"5596","article-title":"Toward robustness against label noise in training deep discriminative neural networks","volume":"30","author":"vahdat","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00718"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00582"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01261-8_14"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/FG.2017.23"},{"key":"ref39","article-title":"Training deep neural networks on noisy labels with bootstrapping","author":"reed","year":"2014"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00571"},{"key":"ref24","first-page":"3546","article-title":"Semi-supervised learning with ladder networks","author":"rasmus","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.186"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2014.09.081"},{"key":"ref25","first-page":"6","article-title":"Temporal ensembling for semi-supervised learning","volume":"4","author":"samuli","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref20","first-page":"896","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","volume":"3","author":"lee","year":"2013","journal-title":"Workshop on Challenges in Representation Learning"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2010.158"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-16142-2_11"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10894"},{"key":"ref27","first-page":"961","article-title":"On symmetric losses for learning from corrupted labels","author":"charoenphakdee","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref29","first-page":"8792","article-title":"Generalized cross entropy loss for training deep neural networks with noisy labels","author":"zhang","year":"0","journal-title":"Proc 32nd Int Conf Neural Inf Process Syst"}],"container-title":["IEEE Transactions on Affective Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5165369\/10254612\/09629313.pdf?arnumber=9629313","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T15:08:14Z","timestamp":1696864094000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9629313\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,1]]},"references-count":55,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/taffc.2021.3131621","relation":{},"ISSN":["1949-3045","2371-9850"],"issn-type":[{"value":"1949-3045","type":"electronic"},{"value":"2371-9850","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,1]]}}}