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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

Useful reading list and references

  • Ledig C, Theis L, Huszar F, et. al. (2017). Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. CVPR.
  • Shi W, Caballero J, Huszár F, et. al. (2016). Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network. CVPR.
  • Yang E. (May, 2019). PyTorch internals. Retrieved from http://blog.ezyang.com/2019/05/pytorch-internals.
  • Yu J, Lin Z, Yang J, et, al.. (2018). Generative Image Inpainting with Contextual Attention. CVPR.
  • Lavin A, Gray S. (2016). Fast Algorithms for Convolutional Neural Networks. CVPR.
  • Warden P. (April 20, 2015). Why GEMM is at the heart of deep learning. Retrieved from https://petewarden.com/2015/04/20/why-gemm-is-at-the-heart-of-deep-learning.
  • Arjovsky M, Bottou L. (2017). Towards Principled Methods for Training Generative Adversarial Networks...
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