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
Table of Contents (14 chapters)
Free Chapter
Section 1: Introduction and Environment Setup
Section 2: Training GANs
Section 3: Application of GANs in Computer Vision, Natural Language Processing, and Audio

Introduction to Generative Models

In this chapter, you will learn the basics of generative models. We will start with a brief description of, and comparison between, discriminative and generative models, in which you will learn about the properties of these models. Then, we will focus on a comparison between generative models, and briefly describe how they have been used to achieve state-of-the-art models in fields such as computer vision and audio.

We will also cover other models, and then we will focus on the building blocks of Generative Adversarial Networks (GANs), their strengths, and limitations. This information is valuable because it can inform our decisions when approaching a machine learning problem with GANs, or when learning some new development in GANs.

We will cover the following topics as we progress with this chapter:

  • Discriminative and generative models compared...