Seongyeon Kim

Phase-shifted adversarial training

Adversarial training (AT) has been considered an imperative component for safely deploying neural network-based applications. However, it typically comes with slow convergence and worse performance on clean samples (i.e., non-adversarial samples). In this work, we analyze the behavior of neural netw…