Byunghoon Kim

Regularizing activations in neural networks via distribution matching with the Wasserstein metric

Regularization and normalization have become indispensable components in training deep neural networks, resulting in faster training and improved generalization performance. We propose the projected error function regularization loss (PER) that encourages activations to follow the standard normal di…