  Adversarial training has been proven to be an effective technique for
improving the adversarial robustness of models. However, there seems to be an
inherent trade-off between optimizing the model for accuracy and robustness. To
this end, we propose Adversarial Concurrent Training (ACT), which employs
adversarial training in a collaborative learning framework whereby we train a
robust model in conjunction with a natural model in a minimax game. ACT
encourages the two models to align their feature space by using the
task-specific decision boundaries and explore the input space more broadly.
Furthermore, the natural model acts as a regularizer, enforcing priors on
features that the robust model should learn. Our analyses on the behavior of
the models show that ACT leads to a robust model with lower model complexity,
higher information compression in the learned representations, and high
posterior entropy solutions indicative of convergence to a flatter minima. We
demonstrate the effectiveness of the proposed approach across different
datasets and network architectures. On ImageNet, ACT achieves 68.20% standard
accuracy and 44.29% robustness accuracy under a 100-iteration untargeted
attack, improving upon the standard adversarial training method's 65.70%
standard accuracy and 42.36% robustness.
