Paper

On Feature Normalization and Data Augmentation

Modern neural network training relies heavily on data augmentation for improved generalization. After the initial success of label-preserving augmentations, there has been a recent surge of interest in label-perturbing approaches, which combine features and labels across training samples to smooth the learned decision surface... (read more)

Results in Papers With Code
(↓ scroll down to see all results)