Squeeze Criterion GANs: Double Adversarial Learning Method

Abstract

Generative adversarial networks (GANs) have attracted much attention since it is able to effective learn from an unknown real distribution. However, the instability of the training process greatly affects the quality of the generated images. To address this problem, the network structure-based, loss-based variant model and some training techniques are proposed. Unfortunately, there are some problems with the above methods, such as the limited effect of stabilizing the training process, the complex mathematical derivation, and the lack of universality of training techniques for different tasks. To this end, we propose a novel squeeze criterion GANs. In this method, we design a pseudo real module to synthesize adversarial sample and the double identity discriminator is designed. Then, the generated image and adversarial sample, as well as the generated image and real image form double adversarial learning. Through double adversarial learning, it forms a squeeze criterion to stabilize the training process of generator and discriminator. Finally, experimental results show that the proposed method has well portability and stabilizes the training process of existing GANs, and improves the quality of generated images.