Mengmeng Sheng

Learning with Imbalanced Noisy Data by Preventing Bias in Sample Selection

2024 · arXiv (Cornell University)

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 studies tend to regard low-loss samples as clean ones and discard …