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…