{"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/metareg-towards-domain-generalization-using","title":"MetaReg: Towards Domain Generalization using Meta-Regularization","arxiv_id":null,"date":"2018-12-01","proceeding":"NeurIPS 2018 12","authors":["Yogesh Balaji","Swami Sankaranarayanan","Rama Chellappa"],"abstract":"Training models that generalize to new domains at test time is a problem of fundamental importance in machine learning. In this work, we encode this notion of domain generalization using a novel regularization function. We pose the problem of finding such a regularization function in a Learning to Learn (or) meta-learning framework. The objective of domain generalization is explicitly modeled by learning a regularizer that makes the model trained on one domain to perform well on another domain. Experimental validations on computer vision and natural language datasets indicate that our method can learn regularizers that achieve good cross-domain generalization.","url_abs":"http://papers.nips.cc/paper/7378-metareg-towards-domain-generalization-using-meta-regularization","url_pdf":"http://papers.nips.cc/paper/7378-metareg-towards-domain-generalization-using-meta-regularization.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":[],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"domain-generalization","task_name":"Domain Generalization"},{"task_slug":"meta-learning","task_name":"Meta-Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/domain-generalization-on-pacs-2","task":"Domain Generalization","dataset":"PACS","model":"MetaReg (Resnet-50)","rank_in_archive_order":68,"of":133,"metrics":{"Average Accuracy":"83.6"},"uses_additional_data":false},{"leaderboard":"/sota/domain-generalization-on-pacs-2","task":"Domain Generalization","dataset":"PACS","model":"MetaReg (Resnet-18)","rank_in_archive_order":83,"of":133,"metrics":{"Average Accuracy":"81.7"},"uses_additional_data":false},{"leaderboard":"/sota/domain-generalization-on-pacs-2","task":"Domain Generalization","dataset":"PACS","model":"MetaReg (Alexnet)","rank_in_archive_order":112,"of":133,"metrics":{"Average Accuracy":"72.62"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}