label noise
A Appendix
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Subclass-Dominant Label Noise: A Counterexample for the Success of Early Stopping Yingbin Bai 1 Zhongyi Han 2 Erkun Y ang 3 Jun Y u 4
Most existing approaches for dealing with noisy labels broadly fall into two categories: noise-modeling-based methods and memorization-effects-based methods.
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Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels
However, these methods typically rely on strict assumptions and are limited to certain types of label noise. In this paper, we reformulate the label-noise problem from a generative-model perspective, i.e., labels are generated by gradually refining an initial random guess.
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CS-Isolate: Extracting Hard Confident Examples by Content and Style Isolation Y exiong Lin 1 Y u Y ao
Label noise widely exists in large-scale image datasets.
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