Learning with Instance-Dependent Noisy Labels by Anchor Hallucination and Hard Sample Label Correction

Po-Hsuan Huang, Chia-Ching Lin, Chih-Fan Hsu +2

Learning from noisy-labeled data is crucial for real-world applications. Traditional Noisy-Label Learning (NLL) methods categorize training data into clean and noisy sets based on…