Excited to share our new work with @dbharathbhushan, @nicolas_courty & more: Wasserstein Adversarial Training for label noise arxiv.org/abs/1904.03936…. We extended the concept of virtual adversarial training with a Wasserstein loss to control the loss evolution between classes. As shown in the previous figure, from the ground cost, one can control the loss evolution between classes with a Wasserstein loss. This is not possible with KL which is isotropic between classes. This control allows us to influence the classifier's decision boundary.