How to Identify and Diagnose GAN Failure Modes
Last Updated on August 17, 2020
How to Identify Unstable Models When Training Generative Adversarial Networks.
GANs are difficult to train.
The reason they are difficult to train is that both the generator model and the discriminator model are trained simultaneously in a zero sum game. This means that improvements to one model come at the expense of the other model.
The goal of training two models involves finding a point of equilibrium between the two competing concerns.
It also means that every time the parameters of one of the models are updated, the nature of the optimization problem that is being solved is changed. This has the effect of creating a dynamic system. In neural network terms, the technical challenge of training two competing neural networks at the same time is that they can fail to converge.
It is important to develop an intuition for both the normal convergence of a GAN model and unusual convergence of GAN models, sometimes called failure modes.
In this tutorial, we will first develop a stable GAN model for a simple image generation task in order to establish what normal convergence looks like and what to expect more generally.