Hands-On Generative Adversarial Networks with Keras

Valle, Rafael

ISBN 10: 1789538203 / ISBN 13: 9781789538205

Published by Packt Publishing, 2019

About this Item

This book will explore deep learning and generative models, and their applications in artificial intelligence. You will learn to evaluate and improve your GAN models by eliminating challenges that are encountered in real-world applications. You will impleme.

Seller Inventory # 288104987

Bibliographic Details

• Title: Hands-On Generative Adversarial Networks with Keras
• Publisher: Packt Publishing
• Publication Date: 2019
• Language: English
• ISBN 10: 1789538203
• ISBN 13: 9781789538205
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Develop generative models for a variety of real-world use-cases and deploy them to production

Key Features

• Discover various GAN architectures using Python and Keras library
• Understand how GAN models function with the help of theoretical and practical examples
• Apply your learnings to become an active contributor to open source GAN applications

Book Description

Generative Adversarial Networks (GANs) have revolutionized the fields of machine learning and deep learning. This book will be your first step towards 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 give you the ability to control characteristics of GAN outputs. 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 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

What you will learn

• Learn how GANs work and the advantages and challenges of working with them
• Control the output of GANs with the help of conditional GANs, using embedding and space manipulation
• Apply GANs to computer vision, NLP, and audio processing
• Understand how to implement progressive growing of GANs
• Use GANs for image synthesis and speech enhancement
• Explore the future of GANs in visual and sonic arts
• Implement pix2pixHD to turn semantic label maps into photorealistic images

Who this book is for

This book is for machine learning practitioners, deep learning researchers, and AI enthusiasts who are looking for a perfect mix of theory and hands-on content in order to implement GANs using Keras. Working knowledge of Python is expected.

Table of Contents

1. Deep Learning Basics and Environment Setup
2. Introduction to Generative Models
3. Implementing your fist GAN
4. Evaluating your first GAN
5. Improving your first GAN
6. Synthesizing and Manipulating Images with GANs
7. Progressive Growing of GANs
8. Natural Language Generation with GANs
9. Text-To-Image Synthesis with GANs
10. Speech Enhancement with GANs
11. TequilaGAN: Identifying GAN samples
12. What's Next in GANs

About the Author

Rafael Valle is a research scientist at NVIDIA focusing on audio applications. He has years of experience developing high performance machine learning models for data/audio analysis, synthesis and machine improvisation with formal specifications. Dr. Valle was the first to generate speech samples from scratch with GANs and to show that simple yet efficient techniques can be used to identify GAN samples. He holds an Interdisciplinary PhD in Machine Listening and Improvisation from UC Berkeley, a Master’s degree in Computer Music from the MH-Stuttgart in Germany and a Bachelor’s degree in Orchestral Conducting from UFRJ in Brazil.