Hands-On Generative Adversarial Networks with Keras

By : Rafael Valle

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Buy this Book

Hands-On Generative Adversarial Networks with Keras

1.5 (2)

By: Rafael Valle

Buy this Book

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

Preface

Who this book is for

What this book covers

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Section 1: Introduction and Environment Setup

Deep Learning Basics and Environment Setup

Deep learning basics

Deep learning environment setup

The deep learning environment test

Summary

Introduction to Generative Models

Discriminative and generative models compared

Generative models

GANs – building blocks

GANs – strengths and weaknesses

Summary

Section 2: Training GANs

Implementing Your First GAN

Technical requirements

Imports

Implementing a Generator and Discriminator

Auxiliary functions

Training your GAN

Summary

Further reading

Evaluating Your First GAN

The evaluation of GANs

Qualitative methods

Quantitative methods

GANs and the birthday paradox

Summary

Improving Your First GAN

Technical requirements

Challenges in training GANs

Tricks of the trade

GAN model architectures

GAN algorithms and loss functions

Summary

Section 3: Application of GANs in Computer Vision, Natural Language Processing, and Audio

Progressive Growing of GANs

Technical requirements

Progressive Growing of GANs

Experimental setup

Model implementation

Summary

Generation of Discrete Sequences Using GANs

Technical requirements

Natural language generation with GANs

Experimental setup

Model implementation

Inference

Summary

Text-to-Image Synthesis with GANs

Technical Requirements

Text-to-image synthesis

Experimental setup

Model implementation

Improving the baseline model

Inference

Summary

TequilaGAN - Identifying GAN Samples

Technical requirements

Identifying GAN samples

Feature extraction

Metrics

Experiments

Summary

References

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

Generation of Discrete Sequences Using GANs

In this chapter, you will learn how to implement a model that is used in the paper Adversarial Generation of Natural Language by Rajeswar et al. This model was first described in the paper Improved Training of Wasserstein GANs by Gulrajani et al. It is capable of generating short discrete sequences with small vocabularies.

We will first address language generation as a problem of conditional probability, in which we want to estimate the probability of the next token given the previous tokens. Then will address the challenges involved in training models for discrete sequences using GANs.

After this introduction to language generation, you will learn how to implement the model described in the paper by Rajeswar et al. and train it on the Google 1 Billion Word Dataset. We will train two separate models: one to generate sequences of characters...