  The pursuit of explaining and improving generalization in deep learning has
elicited efforts both in regularization techniques as well as visualization
techniques of the loss surface geometry. The latter is related to the intuition
prevalent in the community that flatter local optima leads to lower
generalization error. In this paper, we harness the state-of-the-art "filter
normalization" technique of loss-surface visualization to qualitatively
understand the consequences of using adversarial training data augmentation as
the explicit regularization technique of choice. Much to our surprise, we
discover that this oft deployed adversarial augmentation technique does not
actually result in "flatter" loss-landscapes, which requires rethinking
adversarial training generalization, and the relationship between
generalization and loss landscapes geometries.

Machine Learning;Cryptography and Security;Machine Learning