GANs
GANs work a lot like an art forger and a museum curator. Every day, the art forger tries to sell some fake art to the museum, and every day the curator tries to distinguish whether a certain piece is real or fake. The forger learns from their failures. By trying to fool the curator and observing what leads to success and failure, they become a better forger. But the curator learns too. By trying to stay ahead of the forger, they become a better curator. As time passes, the forgeries become better and so does the distinguishing process. After years of battle, the art forger is an expert that can draw just as well as Picasso and the curator is an expert that can distinguish a real painting by tiny details.
Technically, a GAN consists of two neural networks: a generator, which produces data from a random latent vector, and a discriminator, which classifies data as "real," that is, stemming from the training set, or "fake," that is, stemming from the generator.
We can visualize a GAN scheme...
