{"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/wasserstein-distance-guided-representation","title":"Wasserstein Distance Guided Representation Learning for Domain Adaptation","arxiv_id":"1707.01217","date":"2017-07-05","proceeding":null,"authors":["Jian Shen","Yanru Qu","Wei-Nan Zhang","Yong Yu"],"abstract":"Domain adaptation aims at generalizing a high-performance learner on a target\ndomain via utilizing the knowledge distilled from a source domain which has a\ndifferent but related data distribution. One solution to domain adaptation is\nto learn domain invariant feature representations while the learned\nrepresentations should also be discriminative in prediction. To learn such\nrepresentations, domain adaptation frameworks usually include a domain\ninvariant representation learning approach to measure and reduce the domain\ndiscrepancy, as well as a discriminator for classification. Inspired by\nWasserstein GAN, in this paper we propose a novel approach to learn domain\ninvariant feature representations, namely Wasserstein Distance Guided\nRepresentation Learning (WDGRL). WDGRL utilizes a neural network, denoted by\nthe domain critic, to estimate empirical Wasserstein distance between the\nsource and target samples and optimizes the feature extractor network to\nminimize the estimated Wasserstein distance in an adversarial manner. The\ntheoretical advantages of Wasserstein distance for domain adaptation lie in its\ngradient property and promising generalization bound. Empirical studies on\ncommon sentiment and image classification adaptation datasets demonstrate that\nour proposed WDGRL outperforms the state-of-the-art domain invariant\nrepresentation learning approaches.","url_abs":"http://arxiv.org/abs/1707.01217v4","url_pdf":"http://arxiv.org/pdf/1707.01217v4.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":"wasserstein-distance-guided-representation","repo_url":"https://github.com/RockySJ/WDGRL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"wasserstein-distance-guided-representation","repo_url":"https://github.com/BalrajAshwath/Adversarial-Domain_Adaptation_Paper_Implementations","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"wasserstein-distance-guided-representation","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":"wasserstein-distance-guided-representation","repo_url":"https://github.com/criteo-research/pytorch-ada","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"wasserstein-distance-guided-representation","repo_url":"https://github.com/jvanvugt/pytorch-domain-adaptation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"wasserstein-distance-guided-representation","repo_url":"https://github.com/yjhong89/Domain-Adaptation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"wasserstein-distance-guided-representation","repo_url":"https://github.com/CtrlZ1/Domain-Adaptation-Algorithms","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"wasserstein-distance-guided-representation","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":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1707.01217","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1707.01217"}},"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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