A Framework of Composite Functional Gradient Methods for Generative Adversarial Models

Generative adversarial networks (GAN) are trained through a minimax game between a generator and a discriminator to generate data that mimics observations. While being widely used, GAN training is known to be empirically unstable. This paper presents a new theory for generative adversarial methods that does not rely on the traditional minimax formulation. Our theory shows that with a strong discriminator, a good generator can be obtained by composite functional gradient learning, so that several distance measures (including the KL divergence and the JS divergence) between the probability distributions of real data and generated data are simultaneously improved after each functional gradient step until converging to zero. This new point of view leads to stable procedures for training generative models. It also gives a new theoretical insight into the original GAN. Empirical results on image generation show the effectiveness of our new method.