Summary

In this chapter, we discussed the main principles of adversarial training, and explained the roles of two players: the generator and discriminator. We described how to model and train them using a minimax approach whose double goal is to force the generator to learn the true data distribution p_data, and get the discriminator to distinguish perfectly between true samples (belonging to p_data) and unacceptable ones. In the same section, we analyzed the inner dynamics of a Generative Adversarial Network and some common problems that can slow down the training process and lead to a sub-optimal final configuration.

One of the most difficult problems experienced with standard GANs arises when the data generating process and the generator distribution have disjointed support. In this case, the Jensen-Shannon divergence becomes constant and doesn't provide precise information about the distance. An excellent alternative is provided by the Wasserstein measure, which is employed in a more efficient...