Constraining pseudo‐label in self‐training unsupervised domain adaptation with energy‐based model

International Journal of Intelligent SystemsVolume 37, Issue 10 p. 8092-8112 RESEARCH ARTICLE Constraining pseudo-label in self-training unsupervised domain adaptation with energy-based model Lingsheng Kong, Lingsheng Kong Changchun Institute of Optics, Fine Mechanics and Physics, of the Chinese Academy of Sciences, Changchun, ChinaSearch for more papers by this authorBo Hu, Bo Hu Department of Accounting, National University of Singapore, SingaporeSearch for more papers by this authorXiongchang Liu, Xiongchang Liu Department of Information and Electrical Engineering, China University of Mining and Technology, Beijing, ChinaSearch for more papers by this authorJun Lu, Jun Lu Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USASearch for more papers by this authorJane You, Jane You Department of Computing, The Hong Kong Polytechnic University, Hong Kong, ChinaSearch for more papers by this authorXiaofeng Liu, Corresponding Author Xiaofeng Liu xliu61@mgh.harvard.edu liuxiaofeng@cmu.edu orcid.org/0000-0002-4514-2016 Gordon Center for Medical Imaging, Harvard University, Cambridge, MA, USA Correspondence Xiaofeng Liu, Gordon Center for Medical Imaging, Harvard University, Cambridge 02138, MA, USA. Email: xliu61@mgh.harvard.edu and liuxiaofeng@cmu.eduSearch for more papers by this author Lingsheng Kong, Lingsheng Kong Changchun Institute of Optics, Fine Mechanics and Physics, of the Chinese Academy of Sciences, Changchun, ChinaSearch for more papers by this authorBo Hu, Bo Hu Department of Accounting, National University of Singapore, SingaporeSearch for more papers by this authorXiongchang Liu, Xiongchang Liu Department of Information and Electrical Engineering, China University of Mining and Technology, Beijing, ChinaSearch for more papers by this authorJun Lu, Jun Lu Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USASearch for more papers by this authorJane You, Jane You Department of Computing, The Hong Kong Polytechnic University, Hong Kong, ChinaSearch for more papers by this authorXiaofeng Liu, Corresponding Author Xiaofeng Liu xliu61@mgh.harvard.edu liuxiaofeng@cmu.edu orcid.org/0000-0002-4514-2016 Gordon Center for Medical Imaging, Harvard University, Cambridge, MA, USA Correspondence Xiaofeng Liu, Gordon Center for Medical Imaging, Harvard University, Cambridge 02138, MA, USA. Email: xliu61@mgh.harvard.edu and liuxiaofeng@cmu.eduSearch for more papers by this author First published: 20 June 2022 https://doi.org/10.1002/int.22930Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Deep learning is usually data starved, and the unsupervised domain adaptation (UDA) is developed to introduce the knowledge in the labeled source domain to the unlabeled target domain. Recently, deep self-training presents a powerful means for UDA, involving an iterative process of predicting the target domain and then taking the confident predictions as hard pseudo-labels for retraining. However, the pseudo-labels are usually unreliable, thus easily leading to deviated solutions with propagated errors. In this paper, we resort to the energy-based model and constrain the training of the unlabeled target sample with an energy function minimization objective. It can be achieved via a simple additional regularization or an energy-based loss. This framework allows us to gain the benefits of the energy-based model, while retaining strong discriminative performance following a plug-and-play fashion. The convergence property and its connection with classification expectation minimization are investigated. We deliver extensive experiments on the most popular and large-scale UDA benchmarks of image classification as well as semantic segmentation to demonstrate its generality and effectiveness. Open Research DATA AVAILABILITY STATEMENT This study does not involves new data collection. All of the data sets used in this study are public available. Volume37, Issue10October 2022Pages 8092-8112 RelatedInformation