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In such a\nsituation, the model will perform poorly on the new data, since the classifier\nis specialized to recognize visual cues specific to the source domain. In this\nwork we explore a solution, named DeepJDOT, to tackle this problem: through a\nmeasure of discrepancy on joint deep representations/labels based on optimal\ntransport, we not only learn new data representations aligned between the\nsource and target domain, but also simultaneously preserve the discriminative\ninformation used by the classifier. We applied DeepJDOT to a series of visual\nrecognition tasks, where it compares favorably against state-of-the-art deep\ndomain adaptation methods.","url_abs":"http://arxiv.org/abs/1803.10081v3","url_pdf":"http://arxiv.org/pdf/1803.10081v3.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":"deepjdot-deep-joint-distribution-optimal","repo_url":"https://github.com/asahi417/DeepDomainAdaptation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"deepjdot-deep-joint-distribution-optimal","repo_url":"https://github.com/bbdamodaran/deepJDOT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"deepjdot-deep-joint-distribution-optimal","repo_url":"https://github.com/CtrlZ1/Domain-Adaptation-Algorithms/tree/main/pytorch-DeepJDOT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"deepjdot-deep-joint-distribution-optimal","repo_url":"https://github.com/CtrlZ1/Domain-Adaptive-CodeBase","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"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-mnist-to-mnist-m","task":"Domain Adaptation","dataset":"MNIST-to-MNIST-M","model":"DeepJDOT","rank_in_archive_order":2,"of":5,"metrics":{"Accuracy":"92.4"},"uses_additional_data":false},{"leaderboard":"/sota/domain-adaptation-on-mnist-to-usps","task":"Domain Adaptation","dataset":"MNIST-to-USPS","model":"DeepJDOT","rank_in_archive_order":11,"of":14,"metrics":{"Accuracy":"95.7"},"uses_additional_data":false},{"leaderboard":"/sota/domain-adaptation-on-svnh-to-mnist","task":"Domain Adaptation","dataset":"SVNH-to-MNIST","model":"DeepJDOT","rank_in_archive_order":5,"of":9,"metrics":{"Accuracy":"96.7"},"uses_additional_data":false},{"leaderboard":"/sota/domain-adaptation-on-usps-to-mnist","task":"Domain Adaptation","dataset":"USPS-to-MNIST","model":"DeepJDOT","rank_in_archive_order":10,"of":14,"metrics":{"Accuracy":"96.4"},"uses_additional_data":false},{"leaderboard":"/sota/domain-adaptation-on-visda2017","task":"Domain Adaptation","dataset":"VisDA2017","model":"DeepJDOT","rank_in_archive_order":27,"of":28,"metrics":{"Accuracy":"66.9"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-domain-adaptation-on-visda2017","task":"Unsupervised Domain Adaptation","dataset":"VisDA2017","model":"DeepJDOT","rank_in_archive_order":12,"of":13,"metrics":{"Accuracy":"66.9"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.10081","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.10081"}},"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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