Bipolar Gan: Double Check the Solution Space and Lighten False Positive Errors in Generative Adversarial Nets

摘要

Generative Adversarial Nets (GAN) and its variations gain their popularity both in application scenarios and the research front. In this paper, we proposed a novel approach which is compatible with previous methods. It can improve the quality of generated images using both Authenticity Discriminator and Falsity Discriminator to double check the solution space. Our experiments exhibited the feasibility of our solution and expressed its ability to improve recently proposed methods.