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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

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789139907
Length 268 pages
Edition 1st Edition
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Author (1):
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Josh Kalin Josh Kalin
Author Profile Icon Josh Kalin
Josh Kalin
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Toc

Table of Contents (10) Chapters Close

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

Introducing Pix2Pix with pseudocode


When image-to-image translation with conditional adversarial networks, released as Pix2Pix, came out in 2016, it was widely praised as a simple style-transfer network that works out of the box. The network requires less parameter tuning than other techniques in the field, and you'll see the power of this network at the end of this chapter. In this recipe, we're going to cover the basics of the algorithm and present pseudocode prior to implementing the actual code.

Getting ready

Grab the Image-to-Image Translation with Conditional Adversarial Networkspaper: https://arxiv.org/pdf/1611.07004.pdf.

Then, read it and move on—we'll cover the basics of implementing the paper in this recipe.

How to do it...

There are two key components to building this network: the discriminator and generator methods. The following graphic shows an illustration of these two networks in action:

The general network architecture for Image-to-Image Translation with Conditional Adversarial...

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