Generative Adversarial Networks Cookbook

Abstract

Developing Generative Adversarial Networks (GANs) is a complex task, and it is often hard to find code that is easy to understand. This book leads you through eight different examples of modern GAN implementation, including CycleGAN, SimGAN, DCGAN, and imitation learning with GANs. Each chapter builds on a common architecture in Python and Keras to explore increasingly difficult GAN architectures in an easy-to-read format.