Samples with low pointwise mutual information between image and label are mostly mislabeled or corrupted, and dropping them before training improves MNIST accuracy by up to 15%.
Co-teaching: R obust training of deep neural networks with extremely noisy labels
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Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information
Samples with low pointwise mutual information between image and label are mostly mislabeled or corrupted, and dropping them before training improves MNIST accuracy by up to 15%.