TL;DR研究了在生成对抗网络中使用相对论鉴别器的优势,在此基础上提出了Relativistic GANs和Relativistic average GANs模型,并发现相对于传统模型,它们能够更加稳定地生成高质量的图像。
Abstract
In standard generative adversarial network (SGAN), the discriminator estimates the probability that the input data is real. The generator is trained to increase the probability that fake data is real. We argue that it should also simultaneously decrease the probability that real data i