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Reliable Label Correction is a Good Booster When Learning with Extremely Noisy Labels

2022 · arXiv (Cornell University)

Learning with noisy labels has aroused much research interest since data annotations, especially for large-scale datasets, may be inevitably imperfect. Recent approaches resort to a semi-supervised learning problem by dividing training samples into clean and …