Learning with Instance-Dependent Noisy Labels by Anchor Hallucination and Hard Sample Label Correction
This addresses noisy-label learning for real-world applications where mislabeling correlates with visual appearance, representing an incremental improvement over existing methods.
The paper tackles learning from instance-dependent noisy labels by distinguishing clean vs. noisy and easy vs. hard samples, using hallucinated anchors to correct hard samples and achieving superior performance on synthetic and real-world datasets.
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.