Sukang Chae

Learning Discriminative Dynamics with Label Corruption for Noisy Label Detection

Label noise commonly found in real-world datasets has a detrimental impact on a model's generalization. To effectively detect incorrectly labeled instances previous works have mostly relied on distinguishable training signals such as training loss as indicators to differentiate between clean and noi…