[2209.14742] Learning Gradient-based Mixup with Extrapolation toward Flatter Minima for Domain Generalization Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2209.14742 (cs) [Submitted on 29 Sep 2022 ( v1 ), last revised 26 Apr 2026 (this version, v2)] Title: Learning Gradient-based Mixup with Extrapolation toward Flatter Minima for Domain Generalization Authors: Danni Peng , Sinno Jialin Pan View a PDF of the paper titled Learning Gradient-based Mixup with Extrapolation toward Flatter Minima for Domain Generalization, by Danni Peng and 1 other authors View PDF HTML (experimental) Abstract: To address distribution shifts between training and test data, domain generalization (DG) leverages multiple source domains to learn a model that generalizes well to unseen domains. However, existing DG methods often overfit to the source domains, partly due to the limited coverage of the expected region in feature space. Motivated by this, we propose performing mixup with data interpolation and extrapolation to cover potentially unseen regions. To prevent the detrimental effects of unconstrained extrapolation, we carefully design a policy to generate the instance weights, named Flatness-aware Gradient-based Mixup (FGMix). The policy relies on gradient-based compatibilities to assign greater weights to instances that carry more invariant information and learn the mixup policy towards flatter minima for better generalization. On the DomainBed benchmark, we validate the efficacy of various designs of FGMix and demonstrate its superiority over other DG algorithms. Comments: 45 pages, 9 figures Subjects: Machine Learning (cs.LG) Cite as: arXiv:2209.14742 [cs.LG] (or arXiv:2209.14742v2 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2209.14742 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Artificial Intelligence (2026) Related DOI : https://doi.org/10.1016/j.artint.2026.104544 Focus to learn more DOI(s) linking to related resources Submission history From: Danni Peng [ view email ] [v1] Thu, 29 Sep 2022 13:01:14 UTC (16,135 KB) [v2] Sun, 26 Apr 2026 07:48:49 UTC (9,136 KB) Full-text links: Access Paper: View a PDF of the paper titled Learning Gradient-based Mixup with Extrapolation toward Flatter Minima for Domain Generalization, by Danni Peng and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2022-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from