  While generative adversarial networks (GAN) have been widely adopted in
various topics, in this paper we generalize the standard GAN to a new
perspective by treating realness as a random variable that can be estimated
from multiple angles. In this generalized framework, referred to as
RealnessGAN, the discriminator outputs a distribution as the measure of
realness. While RealnessGAN shares similar theoretical guarantees with the
standard GAN, it provides more insights on adversarial learning. Compared to
multiple baselines, RealnessGAN provides stronger guidance for the generator,
achieving improvements on both synthetic and real-world datasets. Moreover, it
enables the basic DCGAN architecture to generate realistic images at 1024*1024
resolution when trained from scratch.
