The concept of GANs is typically introduced using the analogy of a two-player game. In this game, there is typically an art expert and an art forger. The goal of the art forger or counterfeiter is to make a convincing-enough fake to fool the art expert and thus win the game. An example of how this was first portrayed as a neural network is as follows:
GAN by Ian and others
In the preceding diagram, the Generator takes the place of the art forger, the one trying to best the art expert, shown as the Discriminator. The Generator uses random noise as a source to generate an image, with a goal that the image is convincing enough to fool the Discriminator. The Discriminator is trained on both real and fake images, and all it does is classify the image as real or fake. The Generator is then trained to build a convincing-enough fake that will fool the Discriminator...
