Exploring Memorization in Adversarial Training

Understand

Deep learning models have a propensity for fitting the entire training set even with random labels, which requires memorization of every training sample.

• In this paper, we explore the memorization effect in adversarial training (AT) for promoting a deeper understanding of model capacity, convergence, generalization, and especially robust overfitting of the adversarially trained models.
• We first demonstrate that deep networks have sufficient capacity to memorize adversarial examples of training data with completely random labels, but not all AT algorithms can converge under the extreme circumstance.
• Our study of AT with random labels motivates further analyses on the convergence and generalization of AT.