{"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/joint-distribution-optimal-transportation-for","title":"Joint Distribution Optimal Transportation for Domain Adaptation","arxiv_id":"1705.08848","date":"2017-05-24","proceeding":"NeurIPS 2017 12","authors":["Nicolas Courty","Rémi Flamary","Amaury Habrard","Alain Rakotomamonjy"],"abstract":"This paper deals with the unsupervised domain adaptation problem, where one\nwants to estimate a prediction function $f$ in a given target domain without\nany labeled sample by exploiting the knowledge available from a source domain\nwhere labels are known. Our work makes the following assumption: there exists a\nnon-linear transformation between the joint feature/label space distributions\nof the two domain $\\mathcal{P}_s$ and $\\mathcal{P}_t$. We propose a solution of\nthis problem with optimal transport, that allows to recover an estimated target\n$\\mathcal{P}^f_t=(X,f(X))$ by optimizing simultaneously the optimal coupling\nand $f$. We show that our method corresponds to the minimization of a bound on\nthe target error, and provide an efficient algorithmic solution, for which\nconvergence is proved. The versatility of our approach, both in terms of class\nof hypothesis or loss functions is demonstrated with real world classification\nand regression problems, for which we reach or surpass state-of-the-art\nresults.","url_abs":"http://arxiv.org/abs/1705.08848v2","url_pdf":"http://arxiv.org/pdf/1705.08848v2.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":"joint-distribution-optimal-transportation-for","repo_url":"https://github.com/rflamary/JDOT","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"joint-distribution-optimal-transportation-for","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"}}],"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":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1705.08848","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1705.08848"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/asahi417/DeepDomainAdaptation","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/rflamary/JDOT","reach":null}],"summary":{"ran_honours":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"0c47286200bf0ec6","entry":"get_data","repo":"rflamary/JDOT","repo_kind":"official","path":"visu_regression.py","file_url":"https://github.com/rflamary/JDOT/blob/HEAD/visu_regression.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"0c47286200bf0ec6"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}