  Although deep neural networks have achieved great performance on
classification tasks, recent studies showed that well trained networks can be
fooled by adding subtle noises. This paper introduces a new approach to improve
neural network robustness by applying the recovery process on top of the
naturally trained classifier. In this approach, images will be intentionally
corrupted by some significant operator and then be recovered before passing
through the classifiers. SARGAN -- an extension on Generative Adversarial
Networks (GAN) is capable of denoising radar signals. This paper will show that
SARGAN can also recover corrupted images by removing the adversarial effects.
Our results show that this approach does improve the performance of naturally
trained networks.
