What is a GAN?

The key intuition of GAN can be easily considered as analogous to art forgery, which is the process of creating works of art (https://en.wikipedia.org/wiki/Art) that are falsely credited to other, usually more famous, artists. GANs train two neural nets simultaneously, as shown in the next diagram. The generator G(Z) makes the forgery, and the discriminator D(Y) can judge how realistic the reproductions based on its observations of authentic pieces of arts and copies are. 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 and a value close to one denotes forgery. G(Z) takes an input from a random noise, Z, and trains itself to fool D into thinking that whatever G(Z) produces is real. So, the goal of training the discriminator D(Y) is to maximize D(Y) for every image from the true data distribution, and to minimize D(Y) for every image not from the true data distribution. So, G...