  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 the loss distribution of training samples.
However, they often neglect that clean samples, especially those with intricate
visual patterns, may also yield substantial losses. This oversight is
particularly significant in datasets with Instance-Dependent Noise (IDN), where
mislabeling probabilities correlate with visual appearance. Our approach
explicitly distinguishes between clean vs.noisy and easy vs. hard samples. We
identify training samples with small losses, assuming they have simple patterns
and correct labels. Utilizing these easy samples, we hallucinate multiple
anchors to select hard samples for label correction. Corrected hard samples,
along with the easy samples, are used as labeled data in subsequent
semi-supervised training. Experiments on synthetic and real-world IDN datasets
demonstrate the superior performance of our method over other state-of-the-art
NLL methods.
