Forward Super-Resolution: How Can GANs Learn Hierarchical Generative Models for Real-World Distributions

Understand

Generative adversarial networks (GANs) are among the most successful models for learning high-complexity, real-world distributions.

• However, in theory, due to the highly non-convex, non-concave landscape of the minmax training objective, GAN remains one of the least understood deep learning models.
• In this work, we formally study how GANs can efficiently learn certain hierarchically generated distributions that are close to the distribution of real-life images.
• We prove that when a distribution has a structure that we refer to as Forward Super-Resolution, then simply training generative adversarial networks using stochastic gradient descent ascent (SGDA) can learn this distribution efficiently, both in sample and time complexities.