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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

Introduction to StackGAN

A StackGAN is named as such because it has two GANs that are stacked together to form a network that is capable of generating high-resolution images. It has two stages, Stage-I and Stage-II. The Stage-I network generates low-resolution images with basic colors and rough sketches, conditioned on a text embedding, while the Stage-II network takes the image generated by the Stage-I network and generates a high-resolution image that is conditioned on a text embedding. Basically, the second network corrects defects and adds compelling details, yielding a more realistic high-resolution image.

We can compare a StackGAN network to the work of a painter. As a painter starts working, they draw primitive shapes such as lines, circles, and rectangles. Then, they try to fill in the colors. As the painting progresses, more and more detail is added. In a StackGAN, Stage...

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