{"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/implicit-class-conditioned-domain-alignment","title":"Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation","arxiv_id":"2006.04996","date":"2020-06-09","proceeding":"ICML 2020 1","authors":["Xiang Jiang","Qicheng Lao","Stan Matwin","Mohammad Havaei"],"abstract":"We present an approach for unsupervised domain adaptation---with a strong focus on practical considerations of within-domain class imbalance and between-domain class distribution shift---from a class-conditioned domain alignment perspective. Current methods for class-conditioned domain alignment aim to explicitly minimize a loss function based on pseudo-label estimations of the target domain. However, these methods suffer from pseudo-label bias in the form of error accumulation. We propose a method that removes the need for explicit optimization of model parameters from pseudo-labels directly. Instead, we present a sampling-based implicit alignment approach, where the sample selection procedure is implicitly guided by the pseudo-labels. Theoretical analysis reveals the existence of a domain-discriminator shortcut in misaligned classes, which is addressed by the proposed implicit alignment approach to facilitate domain-adversarial learning. Empirical results and ablation studies confirm the effectiveness of the proposed approach, especially in the presence of within-domain class imbalance and between-domain class distribution shift.","url_abs":"https://arxiv.org/abs/2006.04996v1","url_pdf":"https://arxiv.org/pdf/2006.04996v1.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":"implicit-class-conditioned-domain-alignment","repo_url":"https://github.com/xiangdal/implicit_alignment","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"pseudo-label","task_name":"Pseudo Label"},{"task_slug":"unsupervised-domain-adaptation","task_name":"Unsupervised Domain Adaptation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/unsupervised-domain-adaptation-on-office-31","task":"Unsupervised Domain Adaptation","dataset":"Office-31","model":"Implicit Alignment (with MDD)","rank_in_archive_order":5,"of":5,"metrics":{"Avg accuracy":"88.8"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-domain-adaptation-on-office-home","task":"Unsupervised Domain Adaptation","dataset":"Office-Home","model":"Implicit Alignment (with MDD)","rank_in_archive_order":19,"of":20,"metrics":{"Avg accuracy":"69.5"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-domain-adaptation-on-office-home-1","task":"Unsupervised Domain Adaptation","dataset":"Office-Home (RS-UT imbalance)","model":"Implicit Alignment (with MDD)","rank_in_archive_order":1,"of":5,"metrics":{"Average Per-Class Accuracy":"61.67"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-domain-adaptation-on-office-home-1","task":"Unsupervised Domain Adaptation","dataset":"Office-Home (RS-UT imbalance)","model":"COAL","rank_in_archive_order":2,"of":5,"metrics":{"Average Per-Class Accuracy":"58.4"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-domain-adaptation-on-office-home-1","task":"Unsupervised Domain Adaptation","dataset":"Office-Home (RS-UT imbalance)","model":"DANN","rank_in_archive_order":3,"of":5,"metrics":{"Average Per-Class Accuracy":"56.91"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-domain-adaptation-on-office-home-1","task":"Unsupervised Domain Adaptation","dataset":"Office-Home (RS-UT imbalance)","model":"MDD","rank_in_archive_order":4,"of":5,"metrics":{"Average Per-Class Accuracy":"55.44"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-domain-adaptation-on-office-home-1","task":"Unsupervised Domain Adaptation","dataset":"Office-Home (RS-UT imbalance)","model":"Source Only","rank_in_archive_order":5,"of":5,"metrics":{"Average Per-Class Accuracy":"52.81"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-domain-adaptation-on-visda2017","task":"Unsupervised Domain Adaptation","dataset":"VisDA2017","model":"Implicit Alignment (with MDD)","rank_in_archive_order":11,"of":13,"metrics":{"Accuracy":"75.8"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2006.04996","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}