Implement GAN architectures to generate images, text, audio, 3D models, and more
Understand how GANs work and become an active contributor in the open source community
Learn how to generate photo-realistic images based on text descriptions

Description

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.

What you will learn

Implement PyTorch s latest features to ensure efficient model designing Get to grips with the working mechanisms of GAN models Perform style transfer between unpaired image collections with CycleGAN Build and train 3D-GANs to generate a point cloud of 3D objects Create a range of GAN models to perform various image synthesis operations Use SEGAN to suppress noise and improve the quality of speech audio

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Product Details

Publication date : Dec 12, 2019

Length 312 pages

Edition : 1st Edition

Language : English

ISBN-13 : 9781789530513

Category :

Data

Languages :

Python (Intermediate)

Concepts :

Data Science

Tools :

PyTorch (Intermediate)

Estimated delivery fee Deliver to United States

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$21.95

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$6.95

15 Chapters

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

1. Section 1: Introduction to GANs and PyTorch

Section 1: Introduction to GANs and PyTorch

2. Generative Adversarial Networks Fundamentals

Generative Adversarial Networks Fundamentals

Fundamentals of machine learning

Generator and discriminator networks

What GAN we do?

Summary

References and useful reading list

3. Getting Started with PyTorch 1.3

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

4. Best Practices for Model Design and Training

Best Practices for Model Design and Training

Model design cheat sheet

Model training cheat sheet

Efficient coding in Python

Summary

5. Section 2: Typical GAN Models for Image Synthesis

Section 2: Typical GAN Models for Image Synthesis

6. Building Your First GAN with PyTorch

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

7. Generating Images Based on Label Information

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

8. Image-to-Image Translation and Its Applications

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

9. Image Restoration with GANs

Image Restoration with GANs

Image super-resolution with SRGAN

Generative image inpainting

Summary

Useful reading list and references

10. Training Your GANs to Break Different Models

Training Your GANs to Break Different Models

Adversarial examples – attacking deep learning models

Generative adversarial examples

Summary

References and further reading list

11. Image Generation from Description Text

Image Generation from Description Text

Text-to-image synthesis with GANs

Generating photo-realistic images with StackGAN++

Summary

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Authors (2)

John Hany

John Hany received his master's degree and bachelor's degree in calculational mathematics at the University of Electronic Science and Technology of China. He majors in pattern recognition and has years of experience in machine learning and computer vision. He has taken part in several practical projects, including intelligent transport systems and facial recognition systems. His current research interests lie in reducing the computation costs of deep neural networks while improving their performance on image classification and detection tasks. He is enthusiastic about open source projects and has contributed to many of them.

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Greg Walters

Greg Walters has been involved with computers and computer programming since 1972. He is well-versed in Visual Basic, Visual Basic .NET, Python, and SQL and is an accomplished user of MySQL, SQLite, Microsoft SQL Server, Oracle, C++, Delphi, Modula-2, Pascal, C, 80x86 Assembler, COBOL, and Fortran. He is a programming trainer and has trained numerous people on many pieces of computer software, including MySQL, Open Database Connectivity, Quattro Pro, Corel Draw!, Paradox, Microsoft Word, Excel, DOS, Windows 3.11, Windows for Workgroups, Windows 95, Windows NT, Windows 2000, Windows XP, and Linux. He is semi-retired and has written over 100 articles for Full Circle Magazine. He is also a musician and loves to cook. He is open to working as a freelancer on various projects.

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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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