{"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/codim-learning-with-noisy-labels-via","title":"CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning","arxiv_id":"2111.11652","date":"2021-11-23","proceeding":null,"authors":["Xin Zhang","Zixuan Liu","Kaiwen Xiao","Tian Shen","Junzhou Huang","Wei Yang","Dimitris Samaras","Xiao Han"],"abstract":"Labels are costly and sometimes unreliable. Noisy label learning, semi-supervised learning, and contrastive learning are three different strategies for designing learning processes requiring less annotation cost. Semi-supervised learning and contrastive learning have been recently demonstrated to improve learning strategies that address datasets with noisy labels. Still, the inner connections between these fields as well as the potential to combine their strengths together have only started to emerge. In this paper, we explore further ways and advantages to fuse them. Specifically, we propose CSSL, a unified Contrastive Semi-Supervised Learning algorithm, and CoDiM (Contrastive DivideMix), a novel algorithm for learning with noisy labels. CSSL leverages the power of classical semi-supervised learning and contrastive learning technologies and is further adapted to CoDiM, which learns robustly from multiple types and levels of label noise. We show that CoDiM brings consistent improvements and achieves state-of-the-art results on multiple benchmarks.","url_abs":"https://arxiv.org/abs/2111.11652v1","url_pdf":"https://arxiv.org/pdf/2111.11652v1.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":[],"tasks":[{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"learning-with-noisy-labels","task_name":"Learning with noisy labels"}],"methods":[{"method_slug":"contrastive-learning","method_name":"Contrastive Learning"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-classification-on-mini-webvision-1-0","task":"Image Classification","dataset":"mini WebVision 1.0","model":"CoDiM-Sup (Inception-ResNet-v2)","rank_in_archive_order":6,"of":47,"metrics":{"ImageNet Top-1 Accuracy":"76.52","ImageNet Top-5 Accuracy":"91.96","Top-1 Accuracy":"80.88","Top-5 Accuracy":"92.48"},"uses_additional_data":false},{"leaderboard":"/sota/image-classification-on-mini-webvision-1-0","task":"Image Classification","dataset":"mini WebVision 1.0","model":"CoDiM-Self  (Inception-ResNet-v2)","rank_in_archive_order":10,"of":47,"metrics":{"ImageNet Top-1 Accuracy":"77.24","ImageNet Top-5 Accuracy":"92.48","Top-1 Accuracy":"80.12","Top-5 Accuracy":"93.52"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2111.11652","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}