An iterative self-learning framework with multiple class prototypes corrects noisy labels and trains a ConvNet on both original and corrected labels, reporting strong accuracy on Clothing1M and Food101-N.
k-means++: The ad- vantages of careful seeding
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Deep Self-Learning From Noisy Labels
An iterative self-learning framework with multiple class prototypes corrects noisy labels and trains a ConvNet on both original and corrected labels, reporting strong accuracy on Clothing1M and Food101-N.