  Adversarial training can improve the robustness of neural networks. Previous
methods focus on a single adversarial training strategy and do not consider the
model property trained by different strategies. By revisiting the previous
methods, we find different adversarial training methods have distinct
robustness for sample instances. For example, a sample instance can be
correctly classified by a model trained using standard adversarial training
(AT) but not by a model trained using TRADES, and vice versa. Based on this
observation, we propose a collaborative adversarial training framework to
improve the robustness of neural networks. Specifically, we use different
adversarial training methods to train robust models and let models interact
with their knowledge during the training process. Collaborative Adversarial
Training (CAT) can improve both robustness and accuracy. Extensive experiments
on various networks and datasets validate the effectiveness of our method. CAT
achieves state-of-the-art adversarial robustness without using any additional
data on CIFAR-10 under the Auto-Attack benchmark. Code is available at
https://github.com/liuxingbin/CAT.
