The generative adversarial network (GAN) is a game theory-inspired neural network architecture that was created by Ian Goodfellow in 2014. It comprises two networks—a generator network and a critic network—both of which compete against each other in a minimax game, which allows both of them to improve simultaneously by trying to better the other.
In the last couple of years, GANs have produced some phenomenal results in tasks such as creating images that are indistinguishable from real images, generating music when given some recordings, and even generating text. But these models are known for being notoriously difficult to train. Let's now find out what exactly GANs are, how they bring about such tremendous results, and what makes them so challenging to train.
As we know, discriminative models learn a conditional distribution...
