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Hands-On Generative Adversarial Networks with PyTorch 1.x

You're reading from   Hands-On Generative Adversarial Networks with PyTorch 1.x Implement next-generation neural networks to build powerful GAN models using Python

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Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781789530513
Length 312 pages
Edition 1st Edition
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Authors (2):
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John Hany John Hany
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John Hany
Greg Walters Greg Walters
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Greg Walters
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Table of Contents (15) Chapters Close

Preface 1. Section 1: Introduction to GANs and PyTorch FREE CHAPTER
2. Generative Adversarial Networks Fundamentals 3. Getting Started with PyTorch 1.3 4. Best Practices for Model Design and Training 5. Section 2: Typical GAN Models for Image Synthesis
6. Building Your First GAN with PyTorch 7. Generating Images Based on Label Information 8. Image-to-Image Translation and Its Applications 9. Image Restoration with GANs 10. Training Your GANs to Break Different Models 11. Image Generation from Description Text 12. Sequence Synthesis with GANs 13. Reconstructing 3D models with GANs 14. Other Books You May Enjoy

Image-to-Image Translation and Its Applications

In this chapter, we will push the label-based image generation to the next level: we will use pixel-wise labeling to perform image-to-image translation and transfer image styles.

You will learn how to use pixel-wise label information to perform image-to-image translation with pix2pix and translate high-resolution images with pix2pixHD. Following this, you will learn how to perform style transfer between unpaired image collections with CycleGAN.

By the end of this chapter, combined with the knowledge from the previous chapter, you will have grasped the core methodology of using image-wise and pixel-wise label information to improve the quality, or manipulate the attributes, of generated images. You will also know how to flexibly design model architectures to accomplish your goals, including generating larger images or transferring...

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