{"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/towards-semi-supervised-learning-with-non","title":"Towards Semi-supervised Learning with Non-random Missing Labels","arxiv_id":"2308.08872","date":"2023-08-17","proceeding":"ICCV 2023 1","authors":["Yue Duan","Zhen Zhao","Lei Qi","Luping Zhou","Lei Wang","Yinghuan Shi"],"abstract":"Semi-supervised learning (SSL) tackles the label missing problem by enabling the effective usage of unlabeled data. While existing SSL methods focus on the traditional setting, a practical and challenging scenario called label Missing Not At Random (MNAR) is usually ignored. In MNAR, the labeled and unlabeled data fall into different class distributions resulting in biased label imputation, which deteriorates the performance of SSL models. In this work, class transition tracking based Pseudo-Rectifying Guidance (PRG) is devised for MNAR. We explore the class-level guidance information obtained by the Markov random walk, which is modeled on a dynamically created graph built over the class tracking matrix. PRG unifies the historical information of class distribution and class transitions caused by the pseudo-rectifying procedure to maintain the model's unbiased enthusiasm towards assigning pseudo-labels to all classes, so as the quality of pseudo-labels on both popular classes and rare classes in MNAR could be improved. Finally, we show the superior performance of PRG across a variety of MNAR scenarios, outperforming the latest SSL approaches combining bias removal solutions by a large margin. Code and model weights are available at https://github.com/NJUyued/PRG4SSL-MNAR.","url_abs":"https://arxiv.org/abs/2308.08872v1","url_pdf":"https://arxiv.org/pdf/2308.08872v1.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":"towards-semi-supervised-learning-with-non","repo_url":"https://github.com/njuyued/prg4ssl-mnar","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"semi-supervised-image-classification","task_name":"Semi-Supervised Image Classification"}],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2308.08872","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.08872"}},"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/NJUyued/PRG4SSL-MNAR","reach":null}],"summary":{"ran":1,"ran_fixture":1,"unverified":1},"by_repo_kind":{"official":{"samples":3,"ran":2,"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":3,"samples":[{"code_sha256_prefix":"0055945cdd05a6cd","entry":"PRG","repo":"NJUyued/PRG4SSL-MNAR","repo_kind":"official","path":"models/prg/prg.py","file_url":"https://github.com/NJUyued/PRG4SSL-MNAR/blob/HEAD/models/prg/prg.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":"0055945cdd05a6cd"}},{"code_sha256_prefix":"8141f436a109e7cb","entry":"consistency_loss_prg","repo":"NJUyued/PRG4SSL-MNAR","repo_kind":"official","path":"models/prg/prg.py","file_url":"https://github.com/NJUyued/PRG4SSL-MNAR/blob/HEAD/models/prg/prg.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"8141f436a109e7cb"}},{"code_sha256_prefix":"c1242c6134c8416c","entry":"GM","repo":"NJUyued/PRG4SSL-MNAR","repo_kind":"official","path":"models/prg/prg.py","file_url":"https://github.com/NJUyued/PRG4SSL-MNAR/blob/HEAD/models/prg/prg.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":"c1242c6134c8416c"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}