{"type":"ScholarlyArticle","title":"Learning from Long-Tailed Noisy Data with Sample Selection and Balanced Loss","authors":[{"name":"Lefan Zhang","slug":"lefan-zhang","given":"Lefan","family":"Zhang","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , China School of Artificial Intelligence ,; Nanjing University , China"},{"name":"Zhang-Hao Tian","slug":"zhang-hao-tian","given":"Zhang-Hao","family":"Tian","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , China School of Artificial Intelligence ,; Nanjing University , China"},{"name":"Wujun Zhou","slug":"wujun-zhou","given":"Wujun","family":"Zhou","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , China School of Artificial Intelligence ,; Nanjing University , China"},{"name":"Wei Wang","slug":"wei-wang","given":"Wei","family":"Wang","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , China School of Artificial Intelligence ,; Nanjing University , China"}],"abstract":"The success of deep learning depends on large-scale and well-curated training data, while data in real-world applications are commonly long-tailed and noisy. Existing methods are usually dependent on label frequency to tackle class imbalance, while the model bias on different classes is not directly related to label frequency and the true label frequency is inaccessible under label noise. To solve this, we propose a robust method for learning from long-tailed noisy data with sample selection and balanced loss. Specifically, we separate the noisy training data into clean labeled set and unlabeled set with sample selection, and train the deep neural network in a semi-supervised manner with a balanced loss based on model bias. Extensive experiments on benchmarks demonstrate that our method outperforms existing state-of-the-art methods.","doi":"10.24963/ijcai.2024/605","url":"https://ijcai.org/proceedings/2024/605","pdf":"https://ijcai.org/proceedings/2024/0605.pdf","markdown":"https://ijcai.org/proceedings/2024/605.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/605","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":605,"pages":{"first":5471,"last":5480},"keywords":["Machine Learning: ML: Classification"],"videos":[],"references":[{"position":1,"raw":"[Arazo et al., 2019] Eric Arazo, Diego Ortego, Paul Albert, Noel O'Connor, and Kevin Mcguinness. 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