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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

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Product type Paperback
Published in Dec 2020
Publisher Packt
ISBN-13 9781838826789
Length 306 pages
Edition 1st Edition
Languages
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Author (1):
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Soon Yau Cheong Soon Yau Cheong
Author Profile Icon Soon Yau Cheong
Soon Yau Cheong
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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

Segmentation map-to-image translation with GauGAN

GauGAN (named after 19th-century painter Paul Gauguin) is a GAN from Nvidia. Speaking of Nvidia, it is one of the handful of companies that has invested heavily in GANs. They have achieved several breakthroughs in this space, including ProgressiveGAN (we'll cover that in Chapter 7, High Fidelity Face Generation), to generate high-resolution images, and StyleGAN for high-fidelity faces.

Their main business is in making graphics chips rather than AI software. Therefore, unlike some other companies, who keep their code and trained models as closely guarded secrets, Nvidia tends to open source their software code to the general public. They have built a web page (http://nvidia-research-mingyuliu.com/gaugan/) to showcase GauGAN, which can generate photorealistic landscape photos from segmentation maps. The following screenshot is taken from their web page.

Feel free to pause this chapter for a bit and have a play with the application...

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