finelybook
GANs in Action: Deep learning with Generative Adversarial Networks
Authors: Jakub Langr - Vladimir Bok
ISBN-10 书号: 1617295566
ISBN-13 书号: 9781617295560
Edition 版本: 1
Publisher Finelybook 出版日期: 2019-10-08
pages 页数: 240 pages
Book Description
GANs in Action teaches you how to build and train your own Generative Adversarial Networks,one of the most important innovations in deep learning. In this book,you’ll learn how to start building your own simple adversarial system as you explore the foundation of GAN architecture: the generator and discriminator networks.
Generative Adversarial Networks,GANs,are an incredible AI technology capable of creating images,sound,and videos that are indistinguishable from the “real thing.” By pitting two neural networks against each other—one to generate fakes and one to spot them—GANs rapidly learn to produce photo-realistic faces and other media objects. With the potential to produce stunningly realistic animations or shocking deepfakes,GANs are a huge step forward in deep learning systems.
GANs in Action teaches you to build and train your own Generative Adversarial Networks. You’ll start by creating simple generator and discriminator networks that are the foundation of GAN architecture. Then,following numerous hands-on examples,you’ll train GANs to generate high-resolution images,image-to-image translation,and targeted data generation. Along the way,you’ll find pro tips for making your system smart,effective,and fast.
What’s inside
Building your first GAN
Handling the progressive growing of GANs
Practical applications of GANs
Troubleshooting your system
Contents
Preface
Acknowledgments
About this book
About the cover illustration
Part 1. Introduction to GANs and generative modeling
Chapter 1. Introduction to GANs
Chapter 2. Intro to generative modeling with autoencoders
Chapter 3. Your first GAN: Generating handwritten digits
Chapter 4. Deep Convolutional GAN
Part 2. Advanced topics in GANs
Chapter 5. Training and common challenges: GANing for success
Chapter 6. Progressing with GANs
Chapter 7. Semi-Supervised GAN
Chapter 8. Conditional GAN
Chapter 9. CycleGAN
Part 3. Where to go from here
Chapter 10. Adversarial examples
Chapter 11. Practical applications of GANs
Chapter 12. Looking ahead
Training Generative Adversarial Networks (GANs)
