{"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/reducing-domain-gap-via-style-agnostic","title":"Reducing Domain Gap by Reducing Style Bias","arxiv_id":"1910.11645","date":"2019-10-25","proceeding":"CVPR 2021 1","authors":["Hyeonseob Nam","Hyunjae Lee","Jongchan Park","Wonjun Yoon","Donggeun Yoo"],"abstract":"Convolutional Neural Networks (CNNs) often fail to maintain their performance when they confront new test domains, which is known as the problem of domain shift. Recent studies suggest that one of the main causes of this problem is CNNs' strong inductive bias towards image styles (i.e. textures) which are sensitive to domain changes, rather than contents (i.e. shapes). Inspired by this, we propose to reduce the intrinsic style bias of CNNs to close the gap between domains. Our Style-Agnostic Networks (SagNets) disentangle style encodings from class categories to prevent style biased predictions and focus more on the contents. Extensive experiments show that our method effectively reduces the style bias and makes the model more robust under domain shift. It achieves remarkable performance improvements in a wide range of cross-domain tasks including domain generalization, unsupervised domain adaptation, and semi-supervised domain adaptation on multiple datasets.","url_abs":"https://arxiv.org/abs/1910.11645v4","url_pdf":"https://arxiv.org/pdf/1910.11645v4.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":"reducing-domain-gap-via-style-agnostic","repo_url":"https://github.com/facebookresearch/DomainBed","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"reducing-domain-gap-via-style-agnostic","repo_url":"https://github.com/hyeonseobnam/sagnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"reducing-domain-gap-via-style-agnostic","repo_url":"https://github.com/hyeonseobnam/style-agnostic-networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"domain-generalization","task_name":"Domain Generalization"},{"task_slug":"image-to-sketch-recognition","task_name":"Image to sketch recognition"},{"task_slug":"inductive-bias","task_name":"Inductive Bias"},{"task_slug":"semi-supervised-domain-adaptation","task_name":"Semi-supervised Domain Adaptation"},{"task_slug":"single-source-domain-generalization","task_name":"Single-Source Domain Generalization"},{"task_slug":"unsupervised-domain-adaptation","task_name":"Unsupervised Domain Adaptation"}],"methods":[{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/domain-generalization-on-office-home","task":"Domain Generalization","dataset":"Office-Home","model":"SagNet (ResNet-18)","rank_in_archive_order":44,"of":45,"metrics":{"Average Accuracy":"62.34"},"uses_additional_data":false},{"leaderboard":"/sota/domain-generalization-on-pacs-2","task":"Domain Generalization","dataset":"PACS","model":"SagNet (Resnet-18)","rank_in_archive_order":71,"of":133,"metrics":{"Average Accuracy":"83.25"},"uses_additional_data":false},{"leaderboard":"/sota/domain-generalization-on-pacs-2","task":"Domain Generalization","dataset":"PACS","model":"SagNet (Resnet-50)","rank_in_archive_order":79,"of":133,"metrics":{"Average Accuracy":"82.3"},"uses_additional_data":false},{"leaderboard":"/sota/domain-generalization-on-pacs-2","task":"Domain Generalization","dataset":"PACS","model":"SagNet (Alexnet)","rank_in_archive_order":104,"of":133,"metrics":{"Average Accuracy":"75.52"},"uses_additional_data":false},{"leaderboard":"/sota/image-to-sketch-recognition-on-pacs","task":"Image to sketch recognition","dataset":"PACS","model":"SagNet (ResNet18)","rank_in_archive_order":6,"of":7,"metrics":{"Accuracy":"40.7"},"uses_additional_data":false},{"leaderboard":"/sota/single-source-domain-generalization-on-pacs","task":"Single-Source Domain Generalization","dataset":"PACS","model":"SagNet (ResNet18)","rank_in_archive_order":9,"of":10,"metrics":{"Accuracy":"61.9"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1910.11645","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}