{"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/learning-to-diversify-for-single-domain","title":"Learning to Diversify for Single Domain Generalization","arxiv_id":"2108.11726","date":"2021-08-26","proceeding":"ICCV 2021 10","authors":["Zijian Wang","Yadan Luo","Ruihong Qiu","Zi Huang","Mahsa Baktashmotlagh"],"abstract":"Domain generalization (DG) aims to generalize a model trained on multiple source (i.e., training) domains to a distributionally different target (i.e., test) domain. In contrast to the conventional DG that strictly requires the availability of multiple source domains, this paper considers a more realistic yet challenging scenario, namely Single Domain Generalization (Single-DG), where only one source domain is available for training. In this scenario, the limited diversity may jeopardize the model generalization on unseen target domains. To tackle this problem, we propose a style-complement module to enhance the generalization power of the model by synthesizing images from diverse distributions that are complementary to the source ones. More specifically, we adopt a tractable upper bound of mutual information (MI) between the generated and source samples and perform a two-step optimization iteratively: (1) by minimizing the MI upper bound approximation for each sample pair, the generated images are forced to be diversified from the source samples; (2) subsequently, we maximize the MI between the samples from the same semantic category, which assists the network to learn discriminative features from diverse-styled images. Extensive experiments on three benchmark datasets demonstrate the superiority of our approach, which surpasses the state-of-the-art single-DG methods by up to 25.14%.","url_abs":"https://arxiv.org/abs/2108.11726v3","url_pdf":"https://arxiv.org/pdf/2108.11726v3.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":"learning-to-diversify-for-single-domain","repo_url":"https://github.com/busername/learning_to_diversify","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"domain-generalization","task_name":"Domain Generalization"},{"task_slug":"photo-to-rest-generalization","task_name":"Photo to Rest Generalization"},{"task_slug":"single-source-domain-generalization","task_name":"Single-Source Domain Generalization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/photo-to-rest-generalization-on-pacs","task":"Photo to Rest Generalization","dataset":"PACS","model":"PACS (AlexNet)","rank_in_archive_order":8,"of":8,"metrics":{"Accuracy":"55.24"},"uses_additional_data":false},{"leaderboard":"/sota/single-source-domain-generalization-on-digits","task":"Single-Source Domain Generalization","dataset":"Digits-five","model":"L2D (LeNet)","rank_in_archive_order":7,"of":7,"metrics":{"Accuracy":"74.46"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2108.11726","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.11726"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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