{"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/associative-domain-adaptation","title":"Associative Domain Adaptation","arxiv_id":"1708.00938","date":"2017-08-02","proceeding":"ICCV 2017 10","authors":["Philip Haeusser","Thomas Frerix","Alexander Mordvintsev","Daniel Cremers"],"abstract":"We propose associative domain adaptation, a novel technique for end-to-end\ndomain adaptation with neural networks, the task of inferring class labels for\nan unlabeled target domain based on the statistical properties of a labeled\nsource domain. Our training scheme follows the paradigm that in order to\neffectively derive class labels for the target domain, a network should produce\nstatistically domain invariant embeddings, while minimizing the classification\nerror on the labeled source domain. We accomplish this by reinforcing\nassociations between source and target data directly in embedding space. Our\nmethod can easily be added to any existing classification network with no\nstructural and almost no computational overhead. We demonstrate the\neffectiveness of our approach on various benchmarks and achieve\nstate-of-the-art results across the board with a generic convolutional neural\nnetwork architecture not specifically tuned to the respective tasks. Finally,\nwe show that the proposed association loss produces embeddings that are more\neffective for domain adaptation compared to methods employing maximum mean\ndiscrepancy as a similarity measure in embedding space.","url_abs":"http://arxiv.org/abs/1708.00938v1","url_pdf":"http://arxiv.org/pdf/1708.00938v1.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":"associative-domain-adaptation","repo_url":"https://github.com/nchungvh/dlcl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"associative-domain-adaptation","repo_url":"https://github.com/stes/torch-associative","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/domain-adaptation-on-synsig-to-gtsrb","task":"Domain Adaptation","dataset":"SYNSIG-to-GTSRB","model":"ASSC","rank_in_archive_order":6,"of":6,"metrics":{"Accuracy":"82.8"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1708.00938","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1708.00938"}},"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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