Training GANs

Training a GAN is like playing this game of two adversaries. The generator is learning to generate good enough fake samples, while the discriminator is working hard to discriminate between real and fake. More formally, this is termed the minimax game, where the value function $V(G, D)$ is described as follows:

This is also called the zero-sum game, which has an equilibrium that is the same as the Nash equilibrium. We can better understand the value function $V(G, D)$ by separating out the objective function for each of the players. The following equations describe individual objective functions:

Where $J^D$ is the discriminator objective function in the classical sense, $J^G$ is the generator objective equal to the negative of the discriminator, and $p_{data}$ ...