{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/source-class-selection-with-label-propagation","title":"Source Class Selection with Label Propagation for Partial Domain Adaptation","arxiv_id":null,"date":"2021-09-01","proceeding":"ICIP 2021 9","authors":["Wang","Qian; Breckon","Toby P."],"abstract":"In traditional unsupervised domain adaptation problems, the target domain is assumed to share the same set of classes as the source domain. In practice, there exist situations where target-domain data are from only a subset of source-domain classes and it is not known which classes the target-domain data belong to since they are unlabeled. This problem has been formulated as Partial Domain Adaptation (PDA) in the literature and is a challenging task due to the negative transfer issue (i.e. source-domain data belonging to the irrelevant classes harm the domain adaptation). \r\nWe address the PDA problem by detecting the outlier classes in the source domain progressively. As a result, the PDA is boiled down to an easier unsupervised domain adaptation problem which can be solved without the issue of negative transfer. Specifically, we employ the locality preserving projection to learn a latent common subspace in which a label propagation algorithm is used to label the target-domain data. The outlier classes can be detected if no target-domain data are labeled as these classes. We remove the detected outlier classes from the source domain and repeat the process for multiple iterations until convergence. Experimental results on commonly used datasets Office31 and Office-Home demonstrate our proposed method achieves state-of-the-art performance with an average accuracy of 98.1\\% and 75.4\\% respectively.","url_abs":"https://breckon.org/toby/publications/papers/wang21pda.pdf","url_pdf":"https://breckon.org/toby/publications/papers/wang21pda.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"source-class-selection-with-label-propagation","repo_url":"https://github.com/hellowangqian/scs-lp-pda","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"partial-domain-adaptation","task_name":"Partial Domain Adaptation"},{"task_slug":"unsupervised-domain-adaptation","task_name":"Unsupervised Domain Adaptation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/partial-domain-adaptation-on-office-31","task":"Partial Domain Adaptation","dataset":"Office-31","model":"SCS-LP","rank_in_archive_order":2,"of":7,"metrics":{"Accuracy (%)":"98.1"},"uses_additional_data":false},{"leaderboard":"/sota/partial-domain-adaptation-on-office-home","task":"Partial Domain Adaptation","dataset":"Office-Home","model":"SCS-LP","rank_in_archive_order":6,"of":11,"metrics":{"Accuracy (%)":"75.4"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}