{"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/exploring-diverse-representations-for-open","title":"Exploring Diverse Representations for Open Set Recognition","arxiv_id":"2401.06521","date":"2024-01-12","proceeding":null,"authors":["Yu Wang","Junxian Mu","Pengfei Zhu","QinGhua Hu"],"abstract":"Open set recognition (OSR) requires the model to classify samples that belong to closed sets while rejecting unknown samples during test. Currently, generative models often perform better than discriminative models in OSR, but recent studies show that generative models may be computationally infeasible or unstable on complex tasks. In this paper, we provide insights into OSR and find that learning supplementary representations can theoretically reduce the open space risk. Based on the analysis, we propose a new model, namely Multi-Expert Diverse Attention Fusion (MEDAF), that learns diverse representations in a discriminative way. MEDAF consists of multiple experts that are learned with an attention diversity regularization term to ensure the attention maps are mutually different. The logits learned by each expert are adaptively fused and used to identify the unknowns through the score function. We show that the differences in attention maps can lead to diverse representations so that the fused representations can well handle the open space. Extensive experiments are conducted on standard and OSR large-scale benchmarks. Results show that the proposed discriminative method can outperform existing generative models by up to 9.5% on AUROC and achieve new state-of-the-art performance with little computational cost. Our method can also seamlessly integrate existing classification models. Code is available at https://github.com/Vanixxz/MEDAF.","url_abs":"https://arxiv.org/abs/2401.06521v1","url_pdf":"https://arxiv.org/pdf/2401.06521v1.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":"exploring-diverse-representations-for-open","repo_url":"https://github.com/vanixxz/medaf","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"open-set-learning","task_name":"Open Set Learning"}],"methods":[{"method_slug":"set","method_name":"SET"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2401.06521","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.06521"}},"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":"deterministic:regex_extraction","url":"https://github.com/Vanixxz/MEDAF","reach":null}],"summary":{"ran":2,"ran_draft_wrong":2,"unverified":2},"by_repo_kind":{"official":{"samples":6,"ran":4,"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":6,"samples":[{"code_sha256_prefix":"f47a18435a890f1f","entry":"BasicBlock","repo":"Vanixxz/MEDAF","repo_kind":"official","path":"core/net.py","file_url":"https://github.com/Vanixxz/MEDAF/blob/HEAD/core/net.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"f47a18435a890f1f"}},{"code_sha256_prefix":"8958b0e98a344103","entry":"Classifier","repo":"Vanixxz/MEDAF","repo_kind":"official","path":"core/net.py","file_url":"https://github.com/Vanixxz/MEDAF/blob/HEAD/core/net.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"8958b0e98a344103"}},{"code_sha256_prefix":"cfe611241567a0eb","entry":"build_backbone","repo":"Vanixxz/MEDAF","repo_kind":"official","path":"core/net.py","file_url":"https://github.com/Vanixxz/MEDAF/blob/HEAD/core/net.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"cfe611241567a0eb"}},{"code_sha256_prefix":"be3fea8e6f5db9c7","entry":"conv1x1","repo":"Vanixxz/MEDAF","repo_kind":"official","path":"core/net.py","file_url":"https://github.com/Vanixxz/MEDAF/blob/HEAD/core/net.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"be3fea8e6f5db9c7"}},{"code_sha256_prefix":"4a979c4201662f10","entry":"MultiBranchNet","repo":"Vanixxz/MEDAF","repo_kind":"official","path":"core/net.py","file_url":"https://github.com/Vanixxz/MEDAF/blob/HEAD/core/net.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"4a979c4201662f10"}},{"code_sha256_prefix":"c7b4544243375fc5","entry":"ResNet","repo":"Vanixxz/MEDAF","repo_kind":"official","path":"core/net.py","file_url":"https://github.com/Vanixxz/MEDAF/blob/HEAD/core/net.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"c7b4544243375fc5"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}