{"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/mlnet-mutual-learning-network-with","title":"MLNet: Mutual Learning Network with Neighborhood Invariance for Universal Domain Adaptation","arxiv_id":"2312.07871","date":"2023-12-13","proceeding":null,"authors":["Yanzuo Lu","Meng Shen","Andy J Ma","Xiaohua Xie","Jian-Huang Lai"],"abstract":"Universal domain adaptation (UniDA) is a practical but challenging problem, in which information about the relation between the source and the target domains is not given for knowledge transfer. Existing UniDA methods may suffer from the problems of overlooking intra-domain variations in the target domain and difficulty in separating between the similar known and unknown class. To address these issues, we propose a novel Mutual Learning Network (MLNet) with neighborhood invariance for UniDA. In our method, confidence-guided invariant feature learning with self-adaptive neighbor selection is designed to reduce the intra-domain variations for more generalizable feature representation. By using the cross-domain mixup scheme for better unknown-class identification, the proposed method compensates for the misidentified known-class errors by mutual learning between the closed-set and open-set classifiers. Extensive experiments on three publicly available benchmarks demonstrate that our method achieves the best results compared to the state-of-the-arts in most cases and significantly outperforms the baseline across all the four settings in UniDA. Code is available at https://github.com/YanzuoLu/MLNet.","url_abs":"https://arxiv.org/abs/2312.07871v4","url_pdf":"https://arxiv.org/pdf/2312.07871v4.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":"mlnet-mutual-learning-network-with","repo_url":"https://github.com/YanzuoLu/MLNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"universal-domain-adaptation","task_name":"Universal Domain Adaptation"}],"methods":[{"method_slug":"mixup","method_name":"Mixup"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/universal-domain-adaptation-on-office-31","task":"Universal Domain Adaptation","dataset":"Office-31","model":"MLNet","rank_in_archive_order":2,"of":12,"metrics":{"H-score":"92.8","Source-Free":"no"},"uses_additional_data":false},{"leaderboard":"/sota/universal-domain-adaptation-on-office-home","task":"Universal Domain Adaptation","dataset":"Office-Home","model":"MLNet","rank_in_archive_order":3,"of":14,"metrics":{"H-Score":"77.4","Source-free":"no","VLM":"no"},"uses_additional_data":false},{"leaderboard":"/sota/universal-domain-adaptation-on-visda2017","task":"Universal Domain Adaptation","dataset":"VisDA2017","model":"MLNet","rank_in_archive_order":4,"of":13,"metrics":{"H-score":"69.9","Source-free":"no"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2312.07871","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.07871"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/YanzuoLu/MLNet","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"0ecebd1a065188e5","entry":"setup_logger","repo":"YanzuoLu/MLNet","repo_kind":"official","path":"utils/logger.py","file_url":"https://github.com/YanzuoLu/MLNet/blob/HEAD/utils/logger.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"0ecebd1a065188e5"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}