{"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/learning-from-multiple-annotator-noisy-labels","title":"Learning from Multiple Annotator Noisy Labels via Sample-wise Label Fusion","arxiv_id":"2207.11327","date":"2022-07-22","proceeding":null,"authors":["Zhengqi Gao","Fan-Keng Sun","Mingran Yang","Sucheng Ren","Zikai Xiong","Marc Engeler","Antonio Burazer","Linda Wildling","Luca Daniel","Duane S. Boning"],"abstract":"Data lies at the core of modern deep learning. The impressive performance of supervised learning is built upon a base of massive accurately labeled data. However, in some real-world applications, accurate labeling might not be viable; instead, multiple noisy labels (instead of one accurate label) are provided by several annotators for each data sample. Learning a classifier on such a noisy training dataset is a challenging task. Previous approaches usually assume that all data samples share the same set of parameters related to annotator errors, while we demonstrate that label error learning should be both annotator and data sample dependent. Motivated by this observation, we propose a novel learning algorithm. The proposed method displays superiority compared with several state-of-the-art baseline methods on MNIST, CIFAR-100, and ImageNet-100. Our code is available at: https://github.com/zhengqigao/Learning-from-Multiple-Annotator-Noisy-Labels.","url_abs":"https://arxiv.org/abs/2207.11327v1","url_pdf":"https://arxiv.org/pdf/2207.11327v1.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":"learning-from-multiple-annotator-noisy-labels","repo_url":"https://github.com/zhengqigao/learning-from-multiple-annotator-noisy-labels","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[{"method_slug":"base","method_name":"BASE"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2207.11327","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.11327"}},"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/zhengqigao/learning-from-multiple-annotator-noisy-labels","reach":null},{"provenance":"deterministic:regex_extraction","url":"https://github.com/zhengqigao/Learningfrom-Multiple-Annotator-Noisy-Labels","reach":{"status":"gone","observed_at":"2026-09-17","how":"tree_404+repo_404"}}],"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":"e407c6fb8dbc4143","entry":"LeNet5Ours","repo":"zhengqigao/learning-from-multiple-annotator-noisy-labels","repo_kind":"official","path":"mnist/utils/model.py","file_url":"https://github.com/zhengqigao/learning-from-multiple-annotator-noisy-labels/blob/HEAD/mnist/utils/model.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":"e407c6fb8dbc4143"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}