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

Progressive Growing of GANs

Progressive Growing of Generative Adversarial Networks (GANs) is a training methodology that is introduced in a context where high-resolution image synthesis was dominated by autoregressive models, such as PixelCNN and Variational Autoencoders (VAEs) – just like the models used in the paper Improved Variational Inference with Inverse Autoregressive Flow (

As we described in earlier chapters, although autoregressive models are able to produce high-quality images, when compared to their counterparts they lack an explicit latent representation that can be directly manipulated. Additionally, due to their autoregressive nature, at the time of inference autoregressive models tend to be slower than their counterparts. On the other hand, VAE-based models have quicker inference but are harder to train, and the VAE-based...