{"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/shuffle-patchmix-augmentation-with-confidence","title":"Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation","arxiv_id":"2505.24216","date":"2025-05-30","proceeding":null,"authors":["Prasanna Reddy Pulakurthi","Majid Rabbani","Jamison Heard","Sohail Dianat","Celso M. de Melo","Raghuveer Rao"],"abstract":"This work investigates Source-Free Domain Adaptation (SFDA), where a model adapts to a target domain without access to source data. A new augmentation technique, Shuffle PatchMix (SPM), and a novel reweighting strategy are introduced to enhance performance. SPM shuffles and blends image patches to generate diverse and challenging augmentations, while the reweighting strategy prioritizes reliable pseudo-labels to mitigate label noise. These techniques are particularly effective on smaller datasets like PACS, where overfitting and pseudo-label noise pose greater risks. State-of-the-art results are achieved on three major benchmarks: PACS, VisDA-C, and DomainNet-126. Notably, on PACS, improvements of 7.3% (79.4% to 86.7%) and 7.2% are observed in single-target and multi-target settings, respectively, while gains of 2.8% and 0.7% are attained on DomainNet-126 and VisDA-C. This combination of advanced augmentation and robust pseudo-label reweighting establishes a new benchmark for SFDA. The code is available at: https://github.com/PrasannaPulakurthi/SPM","url_abs":"https://arxiv.org/abs/2505.24216v1","url_pdf":"https://arxiv.org/pdf/2505.24216v1.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":"shuffle-patchmix-augmentation-with-confidence","repo_url":"https://github.com/PrasannaPulakurthi/SPM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"pseudo-label","task_name":"Pseudo Label"},{"task_slug":"source-free-domain-adaptation","task_name":"Source-Free Domain Adaptation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/domain-adaptation-on-domainnet-1","task":"Domain Adaptation","dataset":"DomainNet","model":"SPM","rank_in_archive_order":1,"of":4,"metrics":{"Accuracy":"71.1"},"uses_additional_data":false},{"leaderboard":"/sota/source-free-domain-adaptation-on-pacs","task":"Source-Free Domain Adaptation","dataset":"PACS","model":"SPM","rank_in_archive_order":1,"of":3,"metrics":{"Average Accuracy":"86.7"},"uses_additional_data":false},{"leaderboard":"/sota/source-free-domain-adaptation-on-visda-2017","task":"Source-Free Domain Adaptation","dataset":"VisDA-2017","model":"SPM","rank_in_archive_order":3,"of":10,"metrics":{"Accuracy":"89.4"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}