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%.
A closer look at memorization in deep networks
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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%.