Mastering Machine Learning Algorithms

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 ...