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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 Restoration with GANs

Have you ever stumbled upon an image (or meme) you really love from the internet that has poor quality and is blurry, and even Google couldn't help you to find a high-resolution version of it? Unless you are one of the few who have spent years learning math and coding, knowing exactly which fractional-order regularization term in your objective equation can be solved by which numerical method, we might as well give GANs a shot!

This chapter will help you to perform image super-resolution with SRGAN to generate high-resolution images from low-resolution ones and use a data prefetcher to speed up data loading and increase your GPU's efficiency during training. You will also learn how to implement your own convolution with several methods, including the direct approach, the FFT-based method, and the im2col method. Later on, we will get to see...

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