{"type":"ScholarlyArticle","title":"ProMix: Combating Label Noise via Maximizing Clean Sample Utility","authors":[{"name":"Ruixuan Xiao","slug":"ruixuan-xiao","given":"Ruixuan","family":"Xiao","affiliation":"Zhejiang University , Hangzhou , China"},{"name":"Yiwen Dong","slug":"yiwen-dong","given":"Yiwen","family":"Dong","affiliation":"Zhejiang University , Hangzhou , China"},{"name":"Haobo Wang","slug":"haobo-wang","given":"Haobo","family":"Wang","affiliation":"Zhejiang University , Hangzhou , China"},{"name":"Lei Feng","slug":"lei-feng","given":"Lei","family":"Feng","affiliation":"Nanyang Technological University , Singapore"},{"name":"Runze Wu","slug":"runze-wu","given":"Runze","family":"Wu","affiliation":"NetEase Fuxi AI Lab , Hangzhou , China"},{"name":"Gang Chen","slug":"gang-chen","given":"Gang","family":"Chen","affiliation":"Zhejiang University , Hangzhou , China"},{"name":"Junbo Zhao","slug":"junbo-zhao","given":"Junbo","family":"Zhao","affiliation":"Zhejiang University , Hangzhou , China"}],"abstract":"Learning with Noisy Labels (LNL) has become an appealing topic, as imperfectly annotated data are relatively cheaper to obtain. Recent state-of-the-art approaches employ specific selection mechanisms to separate clean and noisy samples and then apply Semi-Supervised Learning (SSL) techniques for improved performance. However, the selection step mostly provides a medium-sized and decent-enough clean subset, which overlooks a rich set of clean samples. To fulfill this, we propose a novel LNL framework ProMix that attempts to maximize the utility of clean samples for boosted performance. Key to our method, we propose a matched high confidence selection technique that selects those examples with high confidence scores and matched predictions with given labels to dynamically expand a base clean sample set. To overcome the potential side effect of excessive clean set selection procedure, we further devise a novel SSL framework that is able to train balanced and unbiased classifiers on the separated clean and noisy samples. Extensive experiments demonstrate that ProMix significantly advances the current state-of-the-art results on multiple benchmarks with different types and levels of noise. It achieves an average improvement of 2.48% on the CIFAR-N dataset.","doi":"10.24963/ijcai.2023/494","url":"https://ijcai.org/proceedings/2023/494","pdf":"https://ijcai.org/proceedings/2023/0494.pdf","markdown":"https://ijcai.org/proceedings/2023/494.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/494","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Machine Learning","track":"Main Track","number":494,"pages":{"first":4442,"last":4450},"keywords":["Machine Learning: ML: Weakly supervised learning"],"videos":[],"references":[{"position":1,"raw":"[Allen-Zhu et al., 2019] Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song. 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