Convergence of Adversarial Training in Overparametrized Neural Networks

NeurIPS 2019 Ruiqi GaoTianle CaiHaochuan LiLiwei WangCho-Jui HsiehJason D. Lee

Neural networks are vulnerable to adversarial examples, i.e. inputs that are imperceptibly perturbed from natural data and yet incorrectly classified by the network. Adversarial training, a heuristic form of robust optimization that alternates between minimization and maximization steps, has proven to be among the most successful methods to train networks to be robust against a pre-defined family of perturbations... (read more)

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