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Generative Adversarial Networks Fundamentals

Generative Adversarial Networks (GANs) have brought about a revolutionary storm in the machine learning (ML) community. They, to some extent, have changed the way people solve practical problems in Computer Vision (CV) and Natural Language Processing (NLP). Before we dive right into the storm, let's prepare you with the fundamental insights of GANs.

In this chapter, you will understand the idea behind adversarial learning and the basic components of a GAN model. You will also get a brief understanding on how GANs work and how it can be built with NumPy.

Before we start exploiting the new features in PyTorch, we will first learn to build a simple GAN with NumPy to generate sine signals so that you may have a profound understanding of the mechanism beneath GANs. By the end of this chapter, you may relax a little as we walk you through...

Key benefits

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.

Who is this book for?

This GAN book is for machine learning practitioners and deep learning researchers looking to get hands-on guidance in implementing GAN models using PyTorch. You’ll become familiar with state-of-the-art GAN architectures with the help of real-world examples. Working knowledge of Python programming language is necessary to grasp the concepts covered in this book.

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

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Hands-On Generative Adversarial Networks with PyTorch 1.x

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Oleg Oct 12, 2020

A good book with clear examples in Pytorch.

Amazon Verified review

Manuel Neto Feb 17, 2022

O livro é excelente. Aborda os principais temas envolvendo GANs com uma explicação de código muito didática.

Anthony May 23, 2021

Thanks!

ML expert Aug 14, 2020

Lots of code are given. But the explanation could have been better. A better explained book like "Generative Deep Learning" by David Foster should be used along with it.

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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Hands-On Generative Adversarial Networks with PyTorch 1.x

Generative Adversarial Networks Fundamentals

Fundamentals of machine learning

Generator and discriminator networks

What GAN we do?

Summary

References and useful reading list

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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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