On-Manifold Adversarial Training for Boosting Generalization

As outlined in previous articles, there seems to be a significant difference between regular, unconstrained adversarial examples and adversarial examples constrained to the data manifold. In this article, I want to demonstrate that adversarial training with on-manifold adversarial examples has the potential to improve generalization if the manifold is known or approximated well enough. As alternative, for more complex datasets, knowledge of parts of the manifold is sufficient, leading to a kind of adversarial data augmentation using affine transformations.

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