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Projected Gradient Descent (PGD) based adversarial training has become one of the most prominent methods for building robust deep neural network models. However, the computational complexity associated with this approach, due to the maximization of the loss function when finding adversaries, is a longstanding problem and may be prohibitive when using larger and more complex models. In this paper we show that the initial phase of adversarial training is redundant and can be replaced with naturaldoi:10.1109/cvprw50498.2020.00398 dblp:conf/cvpr/GuptaDV20 fatcat:pkmd3uw7ajbrjgpg7fwkrufy5y