{"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/meta-label-correction-for-learning-with-weak-1","title":"Meta Label Correction for Noisy Label Learning","arxiv_id":"1911.03809","date":"2019-11-10","proceeding":null,"authors":["Guoqing Zheng","Ahmed Hassan Awadallah","Susan Dumais"],"abstract":"Leveraging weak or noisy supervision for building effective machine learning models has long been an important research problem. Its importance has further increased recently due to the growing need for large-scale datasets to train deep learning models. Weak or noisy supervision could originate from multiple sources including non-expert annotators or automatic labeling based on heuristics or user interaction signals. There is an extensive amount of previous work focusing on leveraging noisy labels. Most notably, recent work has shown impressive gains by using a meta-learned instance re-weighting approach where a meta-learning framework is used to assign instance weights to noisy labels. In this paper, we extend this approach via posing the problem as label correction problem within a meta-learning framework. We view the label correction procedure as a meta-process and propose a new meta-learning based framework termed MLC (Meta Label Correction) for learning with noisy labels. Specifically, a label correction network is adopted as a meta-model to produce corrected labels for noisy labels while the main model is trained to leverage the corrected labeled. Both models are jointly trained by solving a bi-level optimization problem. We run extensive experiments with different label noise levels and types on both image recognition and text classification tasks. We compare the reweighing and correction approaches showing that the correction framing addresses some of the limitation of reweighting. We also show that the proposed MLC approach achieves large improvements over previous methods in many settings.","url_abs":"https://arxiv.org/abs/1911.03809v2","url_pdf":"https://arxiv.org/pdf/1911.03809v2.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":"meta-label-correction-for-learning-with-weak-1","repo_url":"https://github.com/microsoft/mlc","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"learning-with-noisy-labels","task_name":"Learning with noisy labels"},{"task_slug":"meta-learning","task_name":"Meta-Learning"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"text-classification-1","task_name":"text-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-classification-on-clothing1m-using","task":"Image Classification","dataset":"Clothing1M (using clean data)","model":"MLC","rank_in_archive_order":9,"of":9,"metrics":{"Accuracy":"75.78%"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1911.03809","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.03809"}},"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/microsoft/mlc","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":11},"by_repo_kind":{"official":{"samples":11,"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":"4fc1daf5ede1c34f","entry":"clone_parameters","repo":"microsoft/mlc","repo_kind":"official","path":"mlc_utils.py","file_url":"https://github.com/microsoft/mlc/blob/HEAD/mlc_utils.py","link_basis":"harvester_set","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":"4fc1daf5ede1c34f"}},{"code_sha256_prefix":"74fa122d15c0f3ce","entry":"flip_labels_C","repo":"microsoft/mlc","repo_kind":"official","path":"CIFAR/load_corrupted_data_mlg.py","file_url":"https://github.com/microsoft/mlc/blob/HEAD/CIFAR/load_corrupted_data_mlg.py","link_basis":"harvester_set","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":"74fa122d15c0f3ce"}},{"code_sha256_prefix":"394e8668227b39c9","entry":"flip_labels_C_two","repo":"microsoft/mlc","repo_kind":"official","path":"CIFAR/load_corrupted_data_mlg.py","file_url":"https://github.com/microsoft/mlc/blob/HEAD/CIFAR/load_corrupted_data_mlg.py","link_basis":"harvester_set","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":"394e8668227b39c9"}},{"code_sha256_prefix":"22905be7a3378c0f","entry":"get_logger","repo":"microsoft/mlc","repo_kind":"official","path":"logger.py","file_url":"https://github.com/microsoft/mlc/blob/HEAD/logger.py","link_basis":"harvester_set","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":"22905be7a3378c0f"}},{"code_sha256_prefix":"b4009156896f530b","entry":"resnet32","repo":"microsoft/mlc","repo_kind":"official","path":"CIFAR/resnet.py","file_url":"https://github.com/microsoft/mlc/blob/HEAD/CIFAR/resnet.py","link_basis":"harvester_set","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":"b4009156896f530b"}},{"code_sha256_prefix":"d8cf974088fe695e","entry":"soft_cross_entropy","repo":"microsoft/mlc","repo_kind":"official","path":"mlc_utils.py","file_url":"https://github.com/microsoft/mlc/blob/HEAD/mlc_utils.py","link_basis":"harvester_set","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":"d8cf974088fe695e"}},{"code_sha256_prefix":"4fdf2253fd80498b","entry":"step_hmlc_K","repo":"microsoft/mlc","repo_kind":"official","path":"mlc.py","file_url":"https://github.com/microsoft/mlc/blob/HEAD/mlc.py","link_basis":"harvester_set","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":"4fdf2253fd80498b"}},{"code_sha256_prefix":"d7fb2de38378a382","entry":"tocuda","repo":"microsoft/mlc","repo_kind":"official","path":"mlc_utils.py","file_url":"https://github.com/microsoft/mlc/blob/HEAD/mlc_utils.py","link_basis":"harvester_set","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":"d7fb2de38378a382"}},{"code_sha256_prefix":"ba6749b25e502f54","entry":"uniform_mix_C","repo":"microsoft/mlc","repo_kind":"official","path":"CIFAR/load_corrupted_data_mlg.py","file_url":"https://github.com/microsoft/mlc/blob/HEAD/CIFAR/load_corrupted_data_mlg.py","link_basis":"harvester_set","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":"ba6749b25e502f54"}},{"code_sha256_prefix":"a679c2cf98f9c0e3","entry":"update_params","repo":"microsoft/mlc","repo_kind":"official","path":"mlc.py","file_url":"https://github.com/microsoft/mlc/blob/HEAD/mlc.py","link_basis":"harvester_set","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":"a679c2cf98f9c0e3"}},{"code_sha256_prefix":"e09eb4814ee96cd9","entry":"update_params_sgd","repo":"microsoft/mlc","repo_kind":"official","path":"mlc.py","file_url":"https://github.com/microsoft/mlc/blob/HEAD/mlc.py","link_basis":"harvester_set","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":"e09eb4814ee96cd9"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}