{"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/complementary-label-learning-for-arbitrary","title":"Complementary-Label Learning for Arbitrary Losses and Models","arxiv_id":"1810.04327","date":"2018-10-10","proceeding":"Proceedings of the 36th International Conference on Machine Learning, 2019 6","authors":["Takashi Ishida","Gang Niu","Aditya Krishna Menon","Masashi Sugiyama"],"abstract":"In contrast to the standard classification paradigm where the true class is given to each training pattern, complementary-label learning only uses training patterns each equipped with a complementary label, which only specifies one of the classes that the pattern does not belong to. The goal of this paper is to derive a novel framework of complementary-label learning with an unbiased estimator of the classification risk, for arbitrary losses and models---all existing methods have failed to achieve this goal. Not only is this beneficial for the learning stage, it also makes model/hyper-parameter selection (through cross-validation) possible without the need of any ordinarily labeled validation data, while using any linear/non-linear models or convex/non-convex loss functions. We further improve the risk estimator by a non-negative correction and gradient ascent trick, and demonstrate its superiority through experiments.","url_abs":"https://arxiv.org/abs/1810.04327v4","url_pdf":"https://arxiv.org/pdf/1810.04327v4.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":"complementary-label-learning-for-arbitrary","repo_url":"https://github.com/takashiishida/comp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-classification-on-kuzushiji-mnist","task":"Image Classification","dataset":"Kuzushiji-MNIST","model":"Complementary-Label Learning","rank_in_archive_order":25,"of":26,"metrics":{"Accuracy":"67.1"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.04327","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.04327"}},"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/takashiishida/comp","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":6},"by_repo_kind":{"official":{"samples":6,"ran":0,"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":"b06fb0b0ff84b1af","entry":"assump_free_loss","repo":"takashiishida/comp","repo_kind":"official","path":"utils_algo.py","file_url":"https://github.com/takashiishida/comp/blob/HEAD/utils_algo.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"b06fb0b0ff84b1af"}},{"code_sha256_prefix":"efa941da0bce2a64","entry":"class_prior","repo":"takashiishida/comp","repo_kind":"official","path":"utils_data.py","file_url":"https://github.com/takashiishida/comp/blob/HEAD/utils_data.py","link_basis":"plan_row","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"efa941da0bce2a64"}},{"code_sha256_prefix":"720751095604a255","entry":"forward_loss","repo":"takashiishida/comp","repo_kind":"official","path":"utils_algo.py","file_url":"https://github.com/takashiishida/comp/blob/HEAD/utils_algo.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"720751095604a255"}},{"code_sha256_prefix":"ae9af00449c026cb","entry":"generate_compl_labels","repo":"takashiishida/comp","repo_kind":"official","path":"utils_data.py","file_url":"https://github.com/takashiishida/comp/blob/HEAD/utils_data.py","link_basis":"plan_row","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ae9af00449c026cb"}},{"code_sha256_prefix":"63e7bcbc02872c9f","entry":"non_negative_loss","repo":"takashiishida/comp","repo_kind":"official","path":"utils_algo.py","file_url":"https://github.com/takashiishida/comp/blob/HEAD/utils_algo.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"63e7bcbc02872c9f"}},{"code_sha256_prefix":"95dcacdb66368895","entry":"prepare_mnist_data","repo":"takashiishida/comp","repo_kind":"official","path":"utils_data.py","file_url":"https://github.com/takashiishida/comp/blob/HEAD/utils_data.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"95dcacdb66368895"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}