Generative adversarial network (GAN) is a generative model consisting of two networks that are jointly trained, called generator and discriminator.
The dynamics between these two networks are like those between a forger and an investigator. The forger tries to produce faithful imitations of authentic works of art, while the investigator tries to distinguish the fakes from the originals. In this analogy, the forger represents the generator and the investigator represents the discriminator. The generator accepts input values belonging to a fixed distribution and tries to produce images similar to those of the dataset. The discriminator tries to distinguish the data created by the generator from those belonging ...
