Book Image

Hands-On Generative Adversarial Networks with Keras

By : Rafael Valle
Book Image

Hands-On Generative Adversarial Networks with Keras

By: Rafael Valle

Overview of this book

Generative Adversarial Networks (GANs) have revolutionized the fields of machine learning and deep learning. This book will be your first step toward understanding GAN architectures and tackling the challenges involved in training them. This book opens with an introduction to deep learning and generative models and their applications in artificial intelligence (AI). You will then learn how to build, evaluate, and improve your first GAN with the help of easy-to-follow examples. The next few chapters will guide you through training a GAN model to produce and improve high-resolution images. You will also learn how to implement conditional GANs that enable you to control characteristics of GAN output. You will build on your knowledge further by exploring a new training methodology for progressive growing of GANs. Moving on, you'll gain insights into state-of-the-art models in image synthesis, speech enhancement, and natural language generation using GANs. In addition to this, you'll be able to identify GAN samples with TequilaGAN. By the end of this book, you will be well-versed with the latest advancements in the GAN framework using various examples and datasets, and you will have developed the skills you need to implement GAN architectures for several tasks and domains, including computer vision, natural language processing (NLP), and audio processing. Foreword by Ting-Chun Wang, Senior Research Scientist, NVIDIA

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Table of Contents (14 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Section 1: Introduction and Environment Setup
Section 1: Introduction and Environment Setup
2
Deep Learning Basics and Environment Setup
Deep Learning Basics and Environment Setup
Deep learning basics
Deep learning environment setup
The deep learning environment test
Summary
3
Introduction to Generative Models
Introduction to Generative Models
Discriminative and generative models compared
Generative models
GANs – building blocks
GANs – strengths and weaknesses
Summary
4
Section 2: Training GANs
Section 2: Training GANs
5
Implementing Your First GAN
Implementing Your First GAN
Technical requirements
Imports
Implementing a Generator and Discriminator
Auxiliary functions
Training your GAN
Summary
Further reading
6
Evaluating Your First GAN
Evaluating Your First GAN
The evaluation of GANs
Qualitative methods
Quantitative methods
GANs and the birthday paradox
Summary
7
Improving Your First GAN
Improving Your First GAN
Technical requirements
Challenges in training GANs
Tricks of the trade
GAN model architectures
GAN algorithms and loss functions
Summary
8
Section 3: Application of GANs in Computer Vision, Natural Language Processing, and Audio
Section 3: Application of GANs in Computer Vision, Natural Language Processing, and Audio
9
Progressive Growing of GANs
Progressive Growing of GANs
Technical requirements
Progressive Growing of GANs
Experimental setup
Model implementation
Summary
10
Generation of Discrete Sequences Using GANs
Generation of Discrete Sequences Using GANs
Technical requirements
Natural language generation with GANs
Experimental setup
Model implementation
Inference
Summary
11
Text-to-Image Synthesis with GANs
Text-to-Image Synthesis with GANs
Technical Requirements
Text-to-image synthesis
Experimental setup
Model implementation
Improving the baseline model
Inference
Summary
12
TequilaGAN - Identifying GAN Samples
TequilaGAN - Identifying GAN Samples
Technical requirements
Identifying GAN samples
Feature extraction
Metrics
Experiments
Summary
References
13
Whats next in GANs
Whats next in GANs
What we've GANed so far
Unanswered questions in GANs
Artistic GANs
Recent and yet-to-be-explored GAN topics
Summary
Closing remarks
Further reading

Summary

In this chapter, we investigated numerical properties of samples produced with adversarial methods, especially Generative Adversarial Networks. We showed that fake samples have properties that are barely noticed within visuals of samples, namely the fact that, due to stochastic gradient descent and the requirements of differentiability, fake samples smoothly approximate the dominating modes of the distribution. We analyzed statistical measures of divergence between real data and other data, and the results showed that even in simple cases – for instance, distribution of pixel intensities – the divergence between training data and fake data is large with respect to test data.

Although not common practice, one could possibly circumvent the difference in support between the real and fake data by training Generators that explicitly sample a distribution that...

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