{"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/mixmatch-domain-adaptaion-prize-winning","title":"MixMatch Domain Adaptaion: Prize-winning solution for both tracks of VisDA 2019 challenge","arxiv_id":"1910.03903","date":"2019-10-09","proceeding":null,"authors":["Danila Rukhovich","Danil Galeev"],"abstract":"We present a domain adaptation (DA) system that can be used in multi-source and semi-supervised settings. Using the proposed method we achieved 2nd place on multi-source track and 3rd place on semi-supervised track of the VisDA 2019 challenge (http://ai.bu.edu/visda-2019/). The source code of the method is available at https://github.com/filaPro/visda2019.","url_abs":"https://arxiv.org/abs/1910.03903v1","url_pdf":"https://arxiv.org/pdf/1910.03903v1.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":"mixmatch-domain-adaptaion-prize-winning","repo_url":"https://github.com/filaPro/visda2019","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1910.03903","atlas_url":"https://app.syntology.ai/?focus=1910.03903","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}