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
1
Section 1: Introduction and Environment Setup
4
Section 2: Training GANs
8
Section 3: Application of GANs in Computer Vision, Natural Language Processing, and Audio

The evaluation of GANs

The evaluation of GANs is important because it helps us understand what the characteristics of the model we trained are and what we can achieve with it. In this chapter, we will be asking these questions:

  • Do the fake samples have an image quality that is similar to the real samples?
  • Do the fake samples have a variety that is similar to the real samples?
  • Do the fake samples satisfy the specifications of the real samples?

Notice that by asking these questions, we can evaluate our model and specify what we can achieve with it. For example, a model with a low variety in samples, but good image quality, can be used, whereas a model with relatively bad image quality but a good variety produces noisy data that can be used to regularize another model and help it to generalize lower quality images.

Despite its relative youth, several publications (Arjovsky and...