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Generative Adversarial Networks Cookbook

You're reading from Generative Adversarial Networks Cookbook Over 100 recipes to build generative models using Python, TensorFlow, and Keras

Product type Paperback

Published in Dec 2018

Publisher Packt

ISBN-13 9781789139907

Length 268 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Artificial Intelligence

Author (1):

Josh Kalin

View More author details

Table of Contents (10) Chapters

Preface

1. What Is a Generative Adversarial Network?

2. Data First, Easy Environment, and Data Prep FREE CHAPTER

3. My First GAN in Under 100 Lines

4. Dreaming of New Outdoor Structures Using DCGAN

5. Pix2Pix Image-to-Image Translation

6. Style Transfering Your Image Using CycleGAN

7. Using Simulated Images To Create Photo-Realistic Eyeballs with SimGAN

8. From Image to 3D Models Using GANs

9. Other Books You May Enjoy

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Pseudocode – how does it work?

This recipe will focus on dissecting the internal pieces of the CycleGAN paper (https://arxiv.org/pdf/1703.10593.pdf)—the structure they propose, simple tips they suggest throughout their development, and any potential metrics that we may want to use in our development for this chapter.

Getting ready

For this recipe, you will simply need to create a folder for this chapter's code in your home directory. As a reminder, ensure that you've completed all of the prerequisite installation steps such as installing Docker, Nvidia-Docker, and the Nvidia drivers. Last, grab the CycleGAN paper (https://arxiv.org/pdf/1703.10593.pdf) and make sure to read it before you go on to the next section.

How to do it...

As with every chapter, I'd like to encourage you to begin by reading the paper that this particular algorithm was derived from. The paper provides a foundation for implementing the paper and grounds assumptions that we make during the development. For instance, we won...

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Authors (1)

Kalin

Kalin

Josh Kalin is a Physicist and Technologist focused on the intersection of robotics and machine learning. Josh works on advanced sensors, industrial robotics, machine learning, and automated vehicle research projects. Josh holds degrees in Physics, Mechanical Engineering, and Computer Science. In his free time, he enjoys working on cars (has owned 36 vehicles and counting), building computers, and learning new techniques in robotics and machine learning (like writing this book).

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