Discriminator-Quality Evaluation GAN

Xuesong Wang, Ke Jin, Yi Kong, C. L. Philip Chen, Yuhu Cheng

Abstract: In existing generative adversarial networks (standard GAN and its variants), the discriminator is trained for recognizing the real data as positive while the generated data as negative. This kind of positive-negative classification criterion ignores the fact that the discriminator is a non-objective evaluator, which means that the image quality evaluated by the discriminator may fluctuate during the whole training progress. Considering this fact, we propose a novel GAN framework called Discriminator-Quality Evaluation GAN (DQE-GAN) by using the discriminator outputs to evaluate image quality. By dynamically classifying images into high discriminator-quality and low discriminator-quality samples, every adversarial iteration step can be more reasonable and objective. The convergence of DQE-GAN framework can be theoretically proved. Through extensive experiments, we demonstrate DQE-GANs’ ability of achieving better generated images faster and more stable.