MMA Training: Direct Input Space Margin Maximization through Adversarial Training

ICLR 2020 Gavin Weiguang DingYash SharmaKry Yik Chau LuiRuitong Huang

We study adversarial robustness of neural networks from a margin maximization perspective, where margins are defined as the distances from inputs to a classifier's decision boundary. Our study shows that maximizing margins can be achieved by minimizing the adversarial loss on the decision boundary at the "shortest successful perturbation", demonstrating a close connection between adversarial losses and the margins... (read more)

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