{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T18:37:30Z","timestamp":1771699050858,"version":"3.50.1"},"reference-count":68,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"Institute of Information and Communications Technology Planning and Evaluation"},{"name":"Korean Government (MSIT), Artificial Intelligence Convergence Innovation Human Resources Development, Inha University","award":["RS-2022-00155915"],"award-info":[{"award-number":["RS-2022-00155915"]}]},{"name":"Korean Government (MSIT), Artificial Intelligence Convergence Innovation Human Resources Development, Inha University","award":["IITP-2021-0-02052"],"award-info":[{"award-number":["IITP-2021-0-02052"]}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Korean Government","award":["2022R1A2C2010095"],"award-info":[{"award-number":["2022R1A2C2010095"]}]},{"name":"Korean Government","award":["2022R1A4A1033549"],"award-info":[{"award-number":["2022R1A4A1033549"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3400150","type":"journal-article","created":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T17:34:48Z","timestamp":1715621688000},"page":"67847-67859","source":"Crossref","is-referenced-by-count":4,"title":["Toward Identity-Invariant Facial Expression Recognition: Disentangled Representation via Mutual Information Perspective"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3838-126X","authenticated-orcid":false,"given":"Daeha","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Inha University, Incheon, Republic of Korea"}]},{"given":"Seongho","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Inha University, Incheon, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8742-3433","authenticated-orcid":false,"given":"Byung Cheol","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Inha University, Incheon, Republic of Korea"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122946"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3411763.3451721"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1002\/0470013494.ch3"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/0092-6566(77)90037-X"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2017.02.001"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.248"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00896"},{"key":"ref8","first-page":"18749","article-title":"Optimal transport-based identity matching for identity-invariant facial expression recognition","volume-title":"Proc. NeurIPS","author":"Kim"},{"issue":"11","key":"ref9","first-page":"1","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1111\/j.2044-8295.1986.tb02199.x"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1093\/oso\/9780195112719.001.0001"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i7.16743"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412172"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP49357.2023.10095679"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01225-0_29"},{"key":"ref16","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"139","author":"Radford"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2017.2740923"},{"key":"ref18","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Krizhevsky"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2020.2986440"},{"key":"ref21","first-page":"485","article-title":"A personalized affective memory model for improving emotion recognition","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Barros"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00505"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00671"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00859"},{"key":"ref25","first-page":"6767","article-title":"BlockGAN: Learning 3D object-aware scene representations from unlabelled images","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Nguyen-Phuoc"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.69.066138"},{"key":"ref27","first-page":"2445","article-title":"Information maximization for few-shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst. Annu. Conf. Neural Inf. Process. Syst.","author":"Boudiaf"},{"key":"ref28","first-page":"1","article-title":"InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"29","author":"Chen"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00402"},{"key":"ref30","article-title":"The information bottleneck method","author":"Tishby","year":"2000","journal-title":"arXiv:physics\/0004057"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i11.17210"},{"key":"ref32","first-page":"27408","article-title":"Reducing information bottleneck for weakly supervised semantic segmentation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Lee"},{"key":"ref33","article-title":"Deep variational information bottleneck","author":"Alemi","year":"2016","journal-title":"arXiv:1612.00410"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00341"},{"key":"ref35","article-title":"Disentangled representation learning with transmitted information bottleneck","author":"Dang","year":"2023","journal-title":"arXiv:2311.01686"},{"key":"ref36","first-page":"7836","article-title":"Unsupervised speech decomposition via triple information bottleneck","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Qian"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00669"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1214\/09-SS057"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i1.19914"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00087"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20050-2_26"},{"key":"ref42","first-page":"1","article-title":"Chaining mutual information and tightening generalization bounds","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Asadi"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00159"},{"key":"ref44","first-page":"7462","article-title":"Implicit neural representations with periodic activation functions","volume-title":"Proc. NIPS","author":"Sitzmann"},{"key":"ref45","first-page":"1","article-title":"A minimax approach to supervised learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"29","author":"Farnia"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(97)00097-X"},{"key":"ref47","article-title":"Auto-encoding variational Bayes","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kingma"},{"key":"ref48","article-title":"Kernel measures of conditional dependence","volume-title":"Proc. Adv. neural Inf. Process. Syst.","volume":"20","author":"Fukumizu"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00533"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3455008"},{"key":"ref51","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. 3rd Int. Conf. Learn. Represent. (ICLR)","author":"Kingma"},{"key":"ref52","first-page":"24261","article-title":"MLP-mixer: An all-MLP architecture for vision","volume":"34","author":"Tolstikhin","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref53","first-page":"297","article-title":"Expression, affect, action unit recognition: Aff-Wild2, multi-task learning and arcface","volume-title":"Proc. Brit. Mach. Vis. Conf.","author":"Kollias"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00453"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"ref56","article-title":"Image counterfactual sensitivity analysis for detecting unintended bias","author":"Denton","year":"2019","journal-title":"arXiv:1906.06439"},{"key":"ref57","article-title":"Latent space smoothing for individually fair representations","author":"Peychev","year":"2021","journal-title":"arXiv:2111.13650"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.245"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2011.12.007"},{"key":"ref60","volume-title":"CLIP: Connecting Text and Images","year":"2021"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01476"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00388"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2956143"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00693"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2022.3197761"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00618"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01965"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.277"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10380310\/10529288.pdf?arnumber=10529288","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,18]],"date-time":"2024-05-18T05:13:37Z","timestamp":1716009217000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10529288\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":68,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3400150","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}