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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

Pix2pixHD – high-resolution image translation

Pix2pixHD was proposed by Ting-Chun Wang, Ming-Yu Liu, and Jun-Yan Zhu, et. al. in their paper, High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs, which was an upgraded version of the pix2pix model. The biggest improvement of pix2pixHD over pix2pix is that it supports image-to-image translation at 2,048x1,024 resolution and with high quality.

Model architecture

To make this happen, they designed a two-stage approach to gradually train and refine the networks, as shown in the following diagram. First, a lower resolution image of 1,024x512 is generated by a generator network, , called the global generator (the red box). Second, the image is enlarged...

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