Direct Adversarial Training for GANs
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There is an interesting discovery that several neural networks are vulnerable to adversarial examples. That is, many machines learning models misclassify the samples with only a little change which will not be noticed by human eyes. Generative adversarial networks (GANs) are the most popular models for image generation by jointly optimizing discriminator and generator. With stability train, some regularization and normalization have been used to let the discriminator satisfy Lipschitz consistency. In this paper, we have analyzed that the generator may produce adversarial examples for discriminator during the training process, which may cause the unstable training of GANs. For this reason, we propose a direct adversarial training method for GANs. At the same time, we prove that this direct adversarial training can limit the lipschitz constant of the discriminator and accelerate the convergence of the generator. We have verified the advanced performs of the method on multiple baseline networks, such as DCGAN, WGAN, WGAN-GP, and WGAN-LP.
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