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Hands-On Generative Adversarial Networks with PyTorch 1.x

By : John Hany, Greg Walters

4.5 (4)

Buy this Book

Hands-On Generative Adversarial Networks with PyTorch 1.x

4.5 (4)

By: John Hany, Greg Walters

Buy this Book

Overview of this book

With continuously evolving research and development, Generative Adversarial Networks (GANs) are the next big thing in the field of deep learning. This book highlights the key improvements in GANs over generative models and guides in making the best out of GANs with the help of hands-on examples. This book starts by taking you through the core concepts necessary to understand how each component of a GAN model works. You'll build your first GAN model to understand how generator and discriminator networks function. As you advance, you'll delve into a range of examples and datasets to build a variety of GAN networks using PyTorch functionalities and services, and become well-versed with architectures, training strategies, and evaluation methods for image generation, translation, and restoration. You'll even learn how to apply GAN models to solve problems in areas such as computer vision, multimedia, 3D models, and natural language processing (NLP). The book covers how to overcome the challenges faced while building generative models from scratch. Finally, you'll also discover how to train your GAN models to generate adversarial examples to attack other CNN and GAN models. By the end of this book, you will have learned how to build, train, and optimize next-generation GAN models and use them to solve a variety of real-world problems.

Preface

Who this book is for

What this book covers

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Free Chapter

Section 1: Introduction to GANs and PyTorch

Generative Adversarial Networks Fundamentals

Fundamentals of machine learning

Generator and discriminator networks

What GAN we do?

Summary

References and useful reading list

Getting Started with PyTorch 1.3

What's new in PyTorch 1.3?

CUDA – GPU acceleration for fast training and evaluation

Installing PyTorch on Windows and Linux

References and useful reading list

Summary

Best Practices for Model Design and Training

Model design cheat sheet

Model training cheat sheet

Efficient coding in Python

Summary

Section 2: Typical GAN Models for Image Synthesis

Building Your First GAN with PyTorch

Introduction to Deep Convolutional GANs

Creating a DCGAN with PyTorch

Model training and evaluation

Moving to larger datasets

Having fun with the generator network

Summary

References and useful reading list

Generating Images Based on Label Information

CGANs – how are labels used?

Generating images from labels with the CGAN

Working with Fashion-MNIST

InfoGAN – unsupervised attribute extraction

References and useful reading list

Summary

Image-to-Image Translation and Its Applications

Using pixel-wise labels to translate images with pix2pix

Pix2pixHD – high-resolution image translation

CycleGAN – image-to-image translation from unpaired collections

Summary

Furthering reading

Image Restoration with GANs

Image super-resolution with SRGAN

Generative image inpainting

Summary

Useful reading list and references

Training Your GANs to Break Different Models

Adversarial examples – attacking deep learning models

Generative adversarial examples

Summary

References and further reading list

Image Generation from Description Text

Text-to-image synthesis with GANs

Generating photo-realistic images with StackGAN++

Summary

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...