In this chapter, we are going to provide a brief introduction to a family of generative models based on some game theory concepts. Their main peculiarity is an adversarial training procedure that is aimed at learning to distinguish between true and fake samples, driving, at the same time, another component that generates samples more and more similar to the training examples.

In particular, we will be discussing:

• Adversarial training and standard Generative Adversarial Networks (GANs)
• Deep Convolutional GANs (DCGAN)
• Wasserstein GANs (WGAN)