{"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/contrastive-adaptation-network-for","title":"Contrastive Adaptation Network for Unsupervised Domain Adaptation","arxiv_id":"1901.00976","date":"2019-01-04","proceeding":"CVPR 2019 6","authors":["Guoliang Kang","Lu Jiang","Yi Yang","Alexander G. Hauptmann"],"abstract":"Unsupervised Domain Adaptation (UDA) makes predictions for the target domain\ndata while manual annotations are only available in the source domain. Previous\nmethods minimize the domain discrepancy neglecting the class information, which\nmay lead to misalignment and poor generalization performance. To address this\nissue, this paper proposes Contrastive Adaptation Network (CAN) optimizing a\nnew metric which explicitly models the intra-class domain discrepancy and the\ninter-class domain discrepancy. We design an alternating update strategy for\ntraining CAN in an end-to-end manner. Experiments on two real-world benchmarks\nOffice-31 and VisDA-2017 demonstrate that CAN performs favorably against the\nstate-of-the-art methods and produces more discriminative features.","url_abs":"http://arxiv.org/abs/1901.00976v2","url_pdf":"http://arxiv.org/pdf/1901.00976v2.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":"contrastive-adaptation-network-for","repo_url":"https://github.com/Darth-Kronos/Unsupervised-Domain-Adaptation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"contrastive-adaptation-network-for","repo_url":"https://github.com/kgl-prml/Contrastive-Adaptation-Network-for-Unsupervised-Domain-Adaptation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"unsupervised-domain-adaptation","task_name":"Unsupervised Domain Adaptation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/domain-adaptation-on-office-31","task":"Domain Adaptation","dataset":"Office-31","model":"Contrastive Adaptation Network","rank_in_archive_order":9,"of":40,"metrics":{"Average Accuracy":"90.6"},"uses_additional_data":false},{"leaderboard":"/sota/domain-adaptation-on-visda2017","task":"Domain Adaptation","dataset":"VisDA2017","model":"CAN","rank_in_archive_order":14,"of":28,"metrics":{"Accuracy":"87.2"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.00976","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.00976"}},"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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