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

Whats next in GANs

Now that you have been deeply exposed to deep learning and Generative Adversarial Networks (GANs), in this chapter, you will learn about the possible future avenues for GANs! We start with a summary of this book, the topics that we covered, and the knowledge that we have gained so far.

Next, we address important open questions related to GANs that are essential for interacting with GAN models. We briefly pose questions related to how important architectures are, whether GANs really learn the target distribution, whether GANs are dependent on the inductive bias of architectures, and how to identify GAN samples.

Following this, we consider the artistic use of GANs in the visual and sonic arts. In the visual arts, we provide examples of painting and video generation; while in the sonic arts, we provide examples of instrument synthesis and music generation.