TensorFlow 1.x Deep Learning Cookbook

So what is a GAN?

The key process of GAN can be easily understood by considering it analogous to art forgery, which is the process of creating works of art which are falsely credited to other, usually more famous, artists. GANs train two neural networks simultaneously.

The Generator G(Z) is the one that makes the forgery happen, and the Discriminator D(Y) is the one that can judge how realistic the reproduction is based on its observations of authentic pieces of art and copies. D(Y) takes an input Y (for instance an image) and expresses a vote to judge how real the input is. In general, a value close to zero denotes real, while a value close to one denotes forgery. G(Z) takes an input from a random noise Z and trains itself to fool D into ...