Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Hands-On Image Generation with TensorFlow

You're reading from   Hands-On Image Generation with TensorFlow A practical guide to generating images and videos using deep learning

Arrow left icon
Product type Paperback
Published in Dec 2020
Publisher Packt
ISBN-13 9781838826789
Length 306 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Soon Yau Cheong Soon Yau Cheong
Author Profile Icon Soon Yau Cheong
Soon Yau Cheong
Arrow right icon
View More author details
Toc

Table of Contents (15) Chapters Close

Preface 1. Section 1: Fundamentals of Image Generation with TensorFlow
2. Chapter 1: Getting Started with Image Generation Using TensorFlow FREE CHAPTER 3. Chapter 2: Variational Autoencoder 4. Chapter 3: Generative Adversarial Network 5. Section 2: Applications of Deep Generative Models
6. Chapter 4: Image-to-Image Translation 7. Chapter 5: Style Transfer 8. Chapter 6: AI Painter 9. Section 3: Advanced Deep Generative Techniques
10. Chapter 7: High Fidelity Face Generation 11. Chapter 8: Self-Attention for Image Generation 12. Chapter 9: Video Synthesis 13. Chapter 10: Road Ahead 14. Other Books You May Enjoy

Chapter 6: AI Painter

In this chapter, we are going to look at two generative adversarial networks (GANs) that could be used to generate and edit images interactively; they are iGAN and GauGAN . The iGAN (interactive GAN) was the first network to demonstrate how to use GANs for interactive image editing and transformation, back in 2016. As GANs were still in fancy at that time, the generated image quality was not impressive as that of today's networks, but the door was opened to the incorporation of GANs into mainstream image editing.

In this chapter, you will be introduced to the concepts behind iGANs and some websites that feature video demonstrations of them. There won't be any code in that section. Then, we will go over a more recent award-winning application called GauGAN, produced by Nvidia in 2019, that gives impressive results in converting semantic segmentation masks into real landscape photos.

We will implement GauGAN from scratch, starting with a new normalization...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at AU $24.99/month. Cancel anytime