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

GANs and the birthday paradox

One of the biggest challenges in evaluating GANs samples is to understand how much of the real distribution the generator has learned. For example, let's consider the size of the support for the set of all the possible images of dogs. Naturally, this set must include millions of dog images that portray combinations of all dog features, including size, breed, hair color, pose, and more.

Assuming there are millions of dogs in real life that we humans perceive as unique, a GAN that has truly learned the distribution of dogs must be able to produce a similar number of unique dog images. Estimating the number of unique images of dogs a GAN is able to produce might seem like a daunting task at first, but researchers have found a brilliant crude estimate of this by using the birthday paradox.

The birthday paradox is commonly addressed in undergraduate...