Author: Rafael Valle
Pub Date: 2019
Size: 30 Mb
Develop generative models for a variety of real-world use-cases and deploy them to production
Generative Adversarial Networks (GANs) have revolutionized the fields of machine learning and deep learning. This book will be your first step towards 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 give you the ability to control characteristics of GAN outputs. 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 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
What you will learn
- Learn how GANs work and the advantages and challenges of working with them
- Control the output of GANs with the help of conditional GANs, using embedding and space manipulation
- Apply GANs to computer vision, NLP, and audio processing
- Understand how to implement progressive growing of GANs
- Use GANs for image synthesis and speech enhancement
- Explore the future of GANs in visual and sonic arts
- Implement pix2pixHD to turn semantic label maps into photorealistic images