Real or Not Real, that is the Question

Understand

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.