{"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/unified-optimal-transport-framework-for","title":"Unified Optimal Transport Framework for Universal Domain Adaptation","arxiv_id":"2210.17067","date":"2022-10-31","proceeding":null,"authors":["Wanxing Chang","Ye Shi","Hoang Duong Tuan","Jingya Wang"],"abstract":"Universal Domain Adaptation (UniDA) aims to transfer knowledge from a source domain to a target domain without any constraints on label sets. Since both domains may hold private classes, identifying target common samples for domain alignment is an essential issue in UniDA. Most existing methods require manually specified or hand-tuned threshold values to detect common samples thus they are hard to extend to more realistic UniDA because of the diverse ratios of common classes. Moreover, they cannot recognize different categories among target-private samples as these private samples are treated as a whole. In this paper, we propose to use Optimal Transport (OT) to handle these issues under a unified framework, namely UniOT. First, an OT-based partial alignment with adaptive filling is designed to detect common classes without any predefined threshold values for realistic UniDA. It can automatically discover the intrinsic difference between common and private classes based on the statistical information of the assignment matrix obtained from OT. Second, we propose an OT-based target representation learning that encourages both global discrimination and local consistency of samples to avoid the over-reliance on the source. Notably, UniOT is the first method with the capability to automatically discover and recognize private categories in the target domain for UniDA. Accordingly, we introduce a new metric H^3-score to evaluate the performance in terms of both accuracy of common samples and clustering performance of private ones. Extensive experiments clearly demonstrate the advantages of UniOT over a wide range of state-of-the-art methods in UniDA.","url_abs":"https://arxiv.org/abs/2210.17067v2","url_pdf":"https://arxiv.org/pdf/2210.17067v2.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":"unified-optimal-transport-framework-for","repo_url":"https://github.com/changwxx/uniot-for-unida","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"universal-domain-adaptation","task_name":"Universal Domain Adaptation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/universal-domain-adaptation-on-domainnet","task":"Universal Domain Adaptation","dataset":"DomainNet","model":"UniOT","rank_in_archive_order":4,"of":12,"metrics":{"H-Score":"52.04","Source-free":"no"},"uses_additional_data":false},{"leaderboard":"/sota/universal-domain-adaptation-on-office-31","task":"Universal Domain Adaptation","dataset":"Office-31","model":"UniOT","rank_in_archive_order":4,"of":12,"metrics":{"H-score":"91.13","Source-Free":"no"},"uses_additional_data":false},{"leaderboard":"/sota/universal-domain-adaptation-on-office-home","task":"Universal Domain Adaptation","dataset":"Office-Home","model":"UniOT","rank_in_archive_order":4,"of":14,"metrics":{"H-Score":"76.57","Source-free":"no","VLM":"no"},"uses_additional_data":false},{"leaderboard":"/sota/universal-domain-adaptation-on-visda2017","task":"Universal Domain Adaptation","dataset":"VisDA2017","model":"UniOT","rank_in_archive_order":8,"of":13,"metrics":{"H-score":"57.32","Source-free":"no"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2210.17067","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.17067"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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