{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T14:57:05Z","timestamp":1777301825117,"version":"3.51.4"},"reference-count":79,"publisher":"Association for Computing Machinery (ACM)","issue":"3","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62306008, 62376087, 32474007"],"award-info":[{"award-number":["62306008, 62376087, 32474007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key Project of Anhui Province\u2019s Science and Technology Innovation Tackle Plan","award":["202423k09020040"],"award-info":[{"award-number":["202423k09020040"]}]},{"name":"Natural Science Youth Project of the Education Department of Anhui Province","award":["2025AHGXZK40156"],"award-info":[{"award-number":["2025AHGXZK40156"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2026,6,30]]},"abstract":"<jats:p>\n                    Single domain generalization aims to develop a robust model trained on a source domain to generalize well on unseen target domains. Recent progress in single domain generalization has focused on expanding the scope of training data through style (e.g., backgrounds) augmentation. However, existing methods are difficult to generate data with large style shifts due to the lack of precise correlation measures between the generated and original data, and they struggle to effectively capture the consistency between the generated and original data when learning feature representations. In this article, we propose a novel Mutual Information-guided Style Augmentation (MISA) based single domain generalization method. Specifically, MISA incorporates a style diversity module, which uses the matrix-based R\u00e9nyi\u2019s\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\alpha\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    -order entropy functionals to compute an approximate mutual information value between the augmented and original data, minimizing it to guide style generator learning. Moreover, MISA combines the merits of the random convolution and affine transformation to further improve the texture diversity of the augmented data. Additionally, MISA introduces a representation learning module, which minimizes the approximate mutual information value between the prediction logits of the original sample and its corresponding residual component to capture the consistency between the generated and original data for feature representation optimization. Using five real-world datasets, the extensive experiments have demonstrated the effectiveness of MISA, in comparison with state-of-the-art methods.\n                  <\/jats:p>","DOI":"10.1145\/3799252","type":"journal-article","created":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T14:57:21Z","timestamp":1772031441000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Mutual Information-Guided Style Augmentation for Single Domain Generalization"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1837-0515","authenticated-orcid":false,"given":"Shuai","family":"Yang","sequence":"first","affiliation":[{"name":"Anhui Agricultural University, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9444-5039","authenticated-orcid":false,"given":"Zhen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Anhui Agricultural University, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2442-4572","authenticated-orcid":false,"given":"Kui","family":"Yu","sequence":"additional","affiliation":[{"name":"Hefei University of Technology, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3768-8203","authenticated-orcid":false,"given":"Lichuan","family":"Gu","sequence":"additional","affiliation":[{"name":"Anhui Agricultural University, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2396-1704","authenticated-orcid":false,"given":"Xindong","family":"Wu","sequence":"additional","affiliation":[{"name":"Hefei University of Technology, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,4,27]]},"reference":[{"key":"e_1_3_1_2_2","volume-title":"Proceedings of the 36th International Conference on Neural Information Processing Systems (NeurIPS \u201922)","author":"Amit Ron","year":"2022","unstructured":"Ron Amit, Baruch Epstein, Shay Moran, and Ron Meir. 2022. 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