{"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/boosting-novel-category-discovery-over-1","title":"Boosting Novel Category Discovery Over Domains with Soft Contrastive Learning and All in One Classifier","arxiv_id":null,"date":"2023-01-01","proceeding":"ICCV 2023 1","authors":["Zelin Zang","Lei Shang","Senqiao Yang","Fei Wang","Baigui Sun","Xuansong Xie","Stan Z. Li"],"abstract":"    Unsupervised domain adaptation (UDA) has proven to be highly effective in transferring knowledge from a label-rich source domain to a label-scarce target domain. However, the presence of additional novel categories in the target domain has led to the development of open-set domain adaptation (ODA) and universal domain adaptation (UNDA). Existing ODA and UNDA methods treat all novel categories as a single, unified unknown class and attempt to detect it during training. However, we found that domain variance can lead to more significant view-noise in unsupervised data augmentation, which affects the effectiveness of contrastive learning (CL) and causes the model to be overconfident in novel category discovery. To address these issues, a framework nameded Soft-contrastive All-in-one Network (SAN) is proposed for ODA and UNDA tasks. SAN includes a novel data-augmentation-based soft contrastive learning (SCL) loss to fine-tune the backbone for feature transfer and a more human-intuitive classifier to improve new class discovery capability. The SCL loss weakens the adverse effects of the data augmentation view-noise problem which is amplified in domain transfer tasks. The All-in-One (AIO) classifier overcomes the overconfidence problem of current mainstream closed-set and open-set classifiers. Visualization and ablation experiments demonstrate the effectiveness of the proposed innovations. Furthermore, extensive experiment results on ODA and UNDA show that SAN outperforms existing state-of-the-art methods.    ","url_abs":"http://openaccess.thecvf.com//content/ICCV2023/html/Zang_Boosting_Novel_Category_Discovery_Over_Domains_with_Soft_Contrastive_Learning_ICCV_2023_paper.html","url_pdf":"http://openaccess.thecvf.com//content/ICCV2023/papers/Zang_Boosting_Novel_Category_Discovery_Over_Domains_with_Soft_Contrastive_Learning_ICCV_2023_paper.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":"boosting-novel-category-discovery-over-1","repo_url":"https://github.com/zangzelin/code_san_share","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"all","task_name":"All"},{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"universal-domain-adaptation","task_name":"Universal Domain Adaptation"},{"task_slug":"unsupervised-domain-adaptation","task_name":"Unsupervised Domain Adaptation"}],"methods":[{"method_slug":"contrastive-learning","method_name":"Contrastive Learning"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/universal-domain-adaptation-on-domainnet","task":"Universal Domain Adaptation","dataset":"DomainNet","model":"SAN","rank_in_archive_order":5,"of":12,"metrics":{"H-Score":"52.0","Source-free":"no"},"uses_additional_data":false},{"leaderboard":"/sota/universal-domain-adaptation-on-office-31","task":"Universal Domain Adaptation","dataset":"Office-31","model":"SAN","rank_in_archive_order":3,"of":12,"metrics":{"H-score":"91.8","Source-Free":"no"},"uses_additional_data":false},{"leaderboard":"/sota/universal-domain-adaptation-on-office-home","task":"Universal Domain Adaptation","dataset":"Office-Home","model":"SAN","rank_in_archive_order":5,"of":14,"metrics":{"H-Score":"75.9","Source-free":"no","VLM":"no"},"uses_additional_data":false},{"leaderboard":"/sota/universal-domain-adaptation-on-visda2017","task":"Universal Domain Adaptation","dataset":"VisDA2017","model":"SAN","rank_in_archive_order":6,"of":13,"metrics":{"H-score":"60.1","Source-free":"no"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}