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Generative Adversarial Networks Projects

You're reading from   Generative Adversarial Networks Projects Build next-generation generative models using TensorFlow and Keras

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781789136678
Length 316 pages
Edition 1st Edition
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Author (1):
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Kailash Ahirwar Kailash Ahirwar
Author Profile Icon Kailash Ahirwar
Kailash Ahirwar
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Table of Contents (11) Chapters Close

Preface 1. Introduction to Generative Adversarial Networks FREE CHAPTER 2. 3D-GAN - Generating Shapes Using GANs 3. Face Aging Using Conditional GAN 4. Generating Anime Characters Using DCGANs 5. Using SRGANs to Generate Photo-Realistic Images 6. StackGAN - Text to Photo-Realistic Image Synthesis 7. CycleGAN - Turn Paintings into Photos 8. Conditional GAN - Image-to-Image Translation Using Conditional Adversarial Networks 9. Predicting the Future of GANs 10. Other Books You May Enjoy

StackGAN - Text to Photo-Realistic Image Synthesis

Text to image synthesis is one of the use cases for Generative Adversarial Networks (GANs) that has many industrial applications, just like the GANs described in previous chapters. Synthesizing images from text descriptions is very hard, as it is very difficult to build a model that can generate images that reflect the meaning of the text. One network that tries to solve this problem is StackGAN. In this chapter, we will implement a StackGAN in the Keras framework, using TensorFlow as the backend.

In this chapter, we will cover the following topics:

  • Introduction to StackGAN
  • The architecture of StackGAN
  • Data gathering and preparation
  • A Keras implementation of StackGAN
  • Training a StackGAN
  • Evaluating the model
  • Practical applications of a pix2pix network
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