{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T03:42:26Z","timestamp":1778816546823,"version":"3.51.4"},"reference-count":48,"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":[{"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"}]},{"DOI":"10.13039\/501100003621","name":"Korea Government","doi-asserted-by":"publisher","award":["RS-2023-00214326"],"award-info":[{"award-number":["RS-2023-00214326"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003621","name":"Korea Government","doi-asserted-by":"publisher","award":["RS-2023-00242528"],"award-info":[{"award-number":["RS-2023-00242528"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3374105","type":"journal-article","created":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T19:15:20Z","timestamp":1709666120000},"page":"36267-36279","source":"Crossref","is-referenced-by-count":27,"title":["Dual Dynamic Consistency Regularization for Semi-Supervised Domain Adaptation"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1818-9203","authenticated-orcid":false,"given":"Ba Hung","family":"Ngo","sequence":"first","affiliation":[{"name":"Graduate School of Data Science, Chonnam National University, Gwangju, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7641-4883","authenticated-orcid":false,"given":"Ba Thinh","family":"Lam","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, University of Science, Ho Chi Minh City, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thanh Huy","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, National Cheng Kung University, Tainan, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2655-8622","authenticated-orcid":false,"given":"Quang Vinh","family":"Dinh","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, Vietnamese&#x2013;German University, Ho Chi Minh City, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8398-1673","authenticated-orcid":false,"given":"Tae Jong","family":"Choi","sequence":"additional","affiliation":[{"name":"Graduate School of Data Science, Chonnam National University, Gwangju, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr46437.2021.00253"},{"key":"ref2","first-page":"114","article-title":"Distilling and refining domain-specific knowledge for semi-supervised domain adaptation","volume-title":"Proc. 33rd Brit. Mach. Vis. Conf. (BMVC)","author":"Kim"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3279132"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2915607"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3202190"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2921030"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2017.11.010"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.05.030"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3136567"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/213"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/WACV51458.2022.00175"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3215889"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00846"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/iccv48922.2021.00878"},{"key":"ref15","first-page":"5089","article-title":"CLDA: Contrastive learning for semi-supervised domain adaptation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Singh"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3110605"},{"key":"ref17","first-page":"1","article-title":"Surprisingly simple semisupervised domain adaptation with pretraining and consistency","volume-title":"Proc. BMVC","author":"Saligrama"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/130"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3111586"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3012152"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3087867"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2865249"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2984777"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2988928"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01499"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/98"},{"key":"ref27","first-page":"5050","article-title":"MixMatch: A holistic approach to semi-supervised learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Berthelot"},{"key":"ref28","first-page":"51","article-title":"FixMatch: Simplifying semisupervised learning with consistency and confidence","volume-title":"Proc. 34th Int. Conf. Neural Inf. Process. Syst.","author":"Sohn"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00814"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58568-6_35"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02308"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611976700.65"},{"key":"ref33","first-page":"1","article-title":"Con2DA: Simplifying semi-supervised domain adaptation by learning consistent and contrastive feature representations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Prez-Carrasco"},{"key":"ref34","first-page":"122","article-title":"An image is worth 16\u00d716 words: Transformers for image recognition at scale","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Dosovitskiy"},{"key":"ref35","article-title":"How to train your ViT? Data, augmentation, and regularization in vision transformers","author":"Steiner","year":"2021","journal-title":"arXiv:2106.10270"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01428"},{"key":"ref37","first-page":"11525","article-title":"Dash: Semi-supervised learning with dynamic thresholding","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xu"},{"key":"ref38","first-page":"896","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","volume-title":"Proc.Workshop Challenges Represent. Learn. (ICML)","volume":"3","author":"Lee"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00305"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.572"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00149"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref44","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"25","author":"Krizhevsky"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"ref47","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.2307\/j.ctvcm4g18.8"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10380310\/10460533.pdf?arnumber=10460533","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T11:36:28Z","timestamp":1711452988000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10460533\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":48,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3374105","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}