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Hands-On Generative Adversarial Networks with PyTorch 1.x
Hands-On Generative Adversarial Networks with PyTorch 1.x

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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Profile Icon John Hany Profile Icon Greg Walters
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$43.99
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Paperback Dec 2019 312 pages 1st Edition
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Arrow left icon
Profile Icon John Hany Profile Icon Greg Walters
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Paperback Dec 2019 312 pages 1st Edition
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Hands-On Generative Adversarial Networks with PyTorch 1.x

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...

Fundamentals of machine learning

To introduce how GANs work, let's use an analogy:

A long, long time ago, there were two neighboring kingdoms on an island. One was called Netland, and the other was called Ganland. Both kingdoms produced fine wine, armor, and weapons. In Netland, the king demanded that the blacksmiths who specialized in making armor worked at the east corner of the castle, while those who made swords worked at the west side so that the lords and knights could choose the best equipment the kingdom had to offer. The king of Ganland, on the other hand, put all of the blacksmiths in the same corner and demanded that the armor makers and sword makers should test their work against each other every day. If a sword broke through the armor, the sword would sell at a good price and the armor would be melted and reforged. If it didn't, the sword would be remade...

Generator and discriminator networks

Here, we will show you the basic components of GANs and explain how they work with/against each other to achieve our goal to generate realistic samples. A typical structure of a GAN is shown in the following diagram. It contains two different networks: a generator network and a discriminator network. The generator network typically takes random noises as input and generates fake samples. Our goal is to let the fake samples be as close to the real samples as possible. That's where the discriminator comes in. The discriminator is, in fact, a classification network, whose job is to tell whether a given sample is fake or real. The generator tries its best to trick and confuse the discriminator to make the wrong decision, while the discriminator tries its best to distinguish the fake samples from the real ones.

In this process, the differences...

What GAN we do?

GANs can do a lot more than generating sine signals. We can apply GANs to address many different practical problems by altering the input and output dimensions of the generator and combining them with other methods. For example, we can generate text and audio (1-dimension), images (2-dimension), video, and 3D models (3-dimension) based on random input. If we keep the same dimension of input and output, we can perform denoising and translation on these types of data. We can feed real data into the generator and let it output data with larger dimensions, for example, image super-resolution. We can also feed one type of data and let it give another type of data, for example, generate audio based on text, generate images based on text, and so on.

Even though it has only been 4 years since GANs first came out (at the time of writing), people have kept working on improving...

Summary

We've covered a tremendous amount of information just in this first chapter. You've seen how GANs came about and have a basic grasp of the roles of generators and discriminators. You've even seen a few examples of some of the things that GANs can do. We've even created a GAN program using just NumPy. Not to mention we now know why Ganland has better blacksmiths and wine.

Next, we'll dive into the wondrous world of PyTorch, what it is, and how to install it.

The following is a list of references and other helpful links.

References and useful reading list

  1. Goodfellow I, Pouget-Abadie J, Mirza M, et al. (2014). Generative adversarial nets. NIPS, 2672-2680.
  2. Wang, J. (2017, Dec 23). Symbolism vs. Connectionism: A Closing Gap in Artificial Intelligence, retrieved from https://wangjieshu.com/2017/12/23/symbol-vs-connectionism-a-closing-gap-in-artificial-intelligence.
  3. Radford A, Metz L, Chintala S. (2015). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv preprint arXiv:1511.06434.
  4. "Dev Nag". (2017, Feb 11). Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch), retrieved from https://medium.com/@devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f.
  5. Brock A, Donahue J, Simonyan K. (2018). Large Scale GAN Training for High Fidelity Natural Image Synthesis. arXiv preprint arXiv:1809.11096.
  6. Isola...
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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
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Table of Contents

14 Chapters
Section 1: Introduction to GANs and PyTorch Chevron down icon Chevron up icon
Generative Adversarial Networks Fundamentals Chevron down icon Chevron up icon
Getting Started with PyTorch 1.3 Chevron down icon Chevron up icon
Best Practices for Model Design and Training Chevron down icon Chevron up icon
Section 2: Typical GAN Models for Image Synthesis Chevron down icon Chevron up icon
Building Your First GAN with PyTorch Chevron down icon Chevron up icon
Generating Images Based on Label Information Chevron down icon Chevron up icon
Image-to-Image Translation and Its Applications Chevron down icon Chevron up icon
Image Restoration with GANs Chevron down icon Chevron up icon
Training Your GANs to Break Different Models Chevron down icon Chevron up icon
Image Generation from Description Text Chevron down icon Chevron up icon
Sequence Synthesis with GANs Chevron down icon Chevron up icon
Reconstructing 3D models with GANs Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
(4 Ratings)
5 star 75%
4 star 0%
3 star 25%
2 star 0%
1 star 0%
Oleg Oct 12, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A good book with clear examples in Pytorch.
Amazon Verified review Amazon
Manuel Neto Feb 17, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
O livro é excelente. Aborda os principais temas envolvendo GANs com uma explicação de código muito didática.
Amazon Verified review Amazon
Anthony May 23, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Thanks!
Amazon Verified review Amazon
ML expert Aug 14, 2020
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
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.
Amazon Verified review Amazon
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