Book Image

Generative Adversarial Networks Cookbook

By : Josh Kalin
Book Image

Generative Adversarial Networks Cookbook

By: Josh Kalin

Overview of this book

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 implementations, including CycleGAN, simGAN, DCGAN, and 2D image to 3D model generation. Each chapter contains useful recipes to build on a common architecture in Python, TensorFlow and Keras to explore increasingly difficult GAN architectures in an easy-to-read format. The book starts by covering the different types of GAN architecture to help you understand how the model works. This book also contains intuitive recipes to help you work with use cases involving DCGAN, Pix2Pix, and so on. To understand these complex applications, you will take different real-world data sets and put them to use. By the end of this book, you will be equipped to deal with the challenges and issues that you may face while working with GAN models, thanks to easy-to-follow code solutions that you can implement right away.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
About Packt

Introduction to using GANs in order to produce 3D models

In this recipe, we're going to cover a few basic techniques for producing 3D models with GANs and we'll see how we can simplify these architectures for learning.

Getting ready

There are three papers that you'll need to review to understand this recipe:

How to do it...

There are two major steps in the process to go from 2D images to 3D voxelized models—encoding and 3D convolutions.

In each section, we'll cover the basics of the concepts that you'll use throughout this chapter.

For a 2D image – learning an encoding space for an image

There are a few key steps when understanding...