most citedLearning with Imbalanced Noisy Data by Preventing Bias in Sample Selection

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cs.LG2024★ 1 cited

Learning with Imbalanced Noisy Data by Preventing Bias in Sample Selection

Huafeng Liu, Mengmeng Sheng, Zeren Sun +3

Learning with noisy labels has gained increasing attention because the inevitable imperfect labels in real-world scenarios can substantially hurt the deep model performance. Recent…

cs.LG2023

Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learning

Mengmeng Sheng, Zeren Sun, Zhenhuang Cai +3

There has been significant attention devoted to the effectiveness of various domains, such as semi-supervised learning, contrastive learning, and meta-learning, in enhancing the pe…