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Existing\nadversarial domain adaptation methods may not effectively align different\ndomains of multimodal distributions native in classification problems. In this\npaper, we present conditional adversarial domain adaptation, a principled\nframework that conditions the adversarial adaptation models on discriminative\ninformation conveyed in the classifier predictions. Conditional domain\nadversarial networks (CDANs) are designed with two novel conditioning\nstrategies: multilinear conditioning that captures the cross-covariance between\nfeature representations and classifier predictions to improve the\ndiscriminability, and entropy conditioning that controls the uncertainty of\nclassifier predictions to guarantee the transferability. With theoretical\nguarantees and a few lines of codes, the approach has exceeded state-of-the-art\nresults on five datasets.","url_abs":"http://arxiv.org/abs/1705.10667v4","url_pdf":"http://arxiv.org/pdf/1705.10667v4.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":"conditional-adversarial-domain-adaptation","repo_url":"https://github.com/thuml/CDAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"conditional-adversarial-domain-adaptation","repo_url":"https://github.com/adapt-python/adapt","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"conditional-adversarial-domain-adaptation","repo_url":"https://github.com/agrija9/deep-unsupervised-domain-adaptation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"conditional-adversarial-domain-adaptation","repo_url":"https://github.com/thuml/Transfer-Learning-Library","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"conditional-adversarial-domain-adaptation","repo_url":"https://github.com/kevinmusgrave/pytorch-adapt","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"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-svhn-to-mnist","task":"Domain Adaptation","dataset":"SVHN-to-MNIST","model":"CDAN","rank_in_archive_order":12,"of":14,"metrics":{"Accuracy":"89.2"},"uses_additional_data":false},{"leaderboard":"/sota/domain-adaptation-on-usps-to-mnist","task":"Domain Adaptation","dataset":"USPS-to-MNIST","model":"CDAN","rank_in_archive_order":7,"of":14,"metrics":{"Accuracy":"98.0"},"uses_additional_data":false},{"leaderboard":"/sota/domain-adaptation-on-visda2017","task":"Domain Adaptation","dataset":"VisDA2017","model":"CDAN","rank_in_archive_order":26,"of":28,"metrics":{"Accuracy":"73.7"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1705.10667","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1705.10667"}},"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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