Training generative adversarial networks by auxiliary adversarial example regulator

Abstract

Many variant Generative Adversarial Networks (GANs) have been proposed to address the problem that models are difficult to be trained, such as a network-based model, loss-based method, and training-based technique. However, these models rarely improve training stability by reducing the instability of the generator and discriminator simultaneously. For this purpose, inspired by the idea of network regulation, we design an auxiliary adversarial example regulator and propose a new training framework of GANs. In this method, to reduce the instability of the generator and discriminator simultaneously, we design a penalty to constrain directly and guide the generator to generate images, and gradually adjust the training of the discriminator by the auxiliary adversarial example regulator. With the designed constraint and discriminator, the generated image gets closer to the real image. Finally, experimental results demonstrate that the proposed method outperforms the baseline models. The code is available at https://github.com/AdleyGan/GAN-AE-P.