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Many variants of adversarial training have been proposed, with most researchfocusing on problems with relatively few classes. In this paper, we propose TwoHead Adversarial Training (THAT), a two-stream adversarial learning networkthat is designed to handle the large-scale many-class ImageNet dataset. Theproposed method trains a network with two heads and two loss functions; one tominimize feature-space domain shift between natural and adversarial images, andone to promote high classification accuracy. This combination delivers ahardened network that achieves state of the art robust accuracy whilemaintaining high natural accuracy on ImageNet. Through extensive experiments,we demonstrate that the proposed framework outperforms alternative methodsunder both standard and "free" adversarial training settings.

THAT: Two Head Adversarial Training for Improving Robustness at Scale