How to Implement GAN Hacks in Keras to Train Stable Models

Last Updated on July 12, 2019

Generative Adversarial Networks, or GANs, are challenging to train.

This is because the architecture involves both a generator and a discriminator model that compete in a zero-sum game. It means that improvements to one model come at the cost of a degrading of performance in the other model. The result is a very unstable training process that can often lead to failure, e.g. a generator that generates the same image all the time or generates nonsense.

As such, there are a number of heuristics or best practices (called “GAN hacks“) that can be used when configuring and training your GAN models. These heuristics are been hard won by practitioners testing and evaluating hundreds or thousands of combinations of configuration operations on a range of problems over many years.

Some of these heuristics can be challenging to implement, especially for beginners.

Further, some or all of them may be required for a given project, although it may not be clear which subset of heuristics should be adopted, requiring experimentation. This means a practitioner must be ready to implement a given heuristic with little notice.

In this tutorial, you will discover how
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