Deep learning systems have gotten really great at identifying patterns in text, images, and video. But applications that create realistic images, natural sentences and paragraphs, or native-quality translations have proven elusive. Generative Adversarial Networks, or GANs, offer a promising solution to these challenges by pairing two competing neural networks' one that generates content and the other that rejects samples that are of poor quality.
GANs in Action: Deep learning with Generative Adversarial Networks teaches you how to build and train your own generative adversarial networks. First, you'll get an introduction to generative modelling and how GANs work, along with an overview of their potential uses. Then, you'll start building your own simple adversarial system, as you explore the foundation of GAN architecture: the generator and discriminator networks.
- Title
- GANs in Action
- Subtitle
- Deep Learning with Generative Adversarial Networks
- Publisher
- Manning
- Author(s)
- Jakub Langr, Vladimir Bok
- Published
- 2019-10-07
- Edition
- 1
- Format
- eBook (pdf, epub, mobi)
- Pages
- 240
- Language
- English
- ISBN-10
- 1617295566
- ISBN-13
- 9781617295560
- License
- Read online for free
- Book Homepage
- Free eBook, Errata, Code, Solutions, etc.
Copyright
Brief Table of Contents
Table of Contents
Preface
Acknowledgments
About this book
About the cover illustration
Part 1. Introduction to GANs and generative modeling
Chapter 1. Introduction to GANs
1.1. What are Generative Adversarial Networks?
1.2. How do GANs work?
1.3. GANs in action
1.4. Why study GANs?
Summary
Chapter 2. Intro to generative modeling with autoencoders
2.1. Introduction to generative modeling
2.2. How do autoencoders function on a high level?
2.3. What are autoencoders to GANs?
2.4. What is an autoencoder made of?
2.5. Usage of autoencoders
2.6. Unsupervised learning
2.7. Code is life
2.8. Why did we try aGAN?
Summary
Chapter 3. Your first GAN: Generating handwritten digits
3.1. Foundations of GANs: Adversarial training
3.2. The Generator and the Discriminator
3.3. GAN training algorithm
3.4. Tutorial: Generating handwritten digits
3.5. Conclusion
Summary
Chapter 4. Deep Convolutional GAN
4.1. Convolutional neural networks
4.2. Brief history of the DCGAN
4.3. Batch normalization
4.4. Tutorial: Generating handwritten digits with DCGAN
4.5. Conclusion
Summary
Part 2. Advanced topics in GANs
Chapter 5. Training and common challenges: GANing for success
5.1. Evaluation
5.2. Training challenges
5.3. Summary of game setups
5.4. Training hacks
Summary
Chapter 6. Progressing with GANs
6.1. Latent space interpolation
6.2. They grow up so fast
6.3. Summary of key innovations
6.4. TensorFlow Hub and hands-on
6.5. Practical applications
Summary
Chapter 7. Semi-Supervised GAN
7.1. Introducing the Semi-Supervised GAN
7.2. Tutorial: Implementing a Semi-Supervised GAN
7.3. Comparison to a fully supervised classifier
7.4. Conclusion
Summary
Chapter 8. Conditional GAN
8.1. Motivation
8.2. What is Conditional GAN?
8.3. Tutorial: Implementing a Conditional GAN
8.4. Conclusion
Summary
Chapter 9. CycleGAN
9.1. Image-to-image translation
9.2. Cycle-consistency loss: There and back aGAN
9.3. Adversarial loss
9.4. Identity loss
9.5. Architecture
9.6. Object-oriented design of GANs
9.7. Tutorial: CycleGAN
9.8. Expansions, augmentations, and applications
Summary
Part 3. Where to go from here
Chapter 10. Adversarial examples
10.1. Context of adversarial examples
10.2. Lies, damned lies, and distributions
10.3. Use and abuse of training
10.4. Signal and the noise
10.5. Not all hope is lost
10.6. Adversaries to GANs
10.7. Conclusion
Summary
Chapter 11. Practical applications of GANs
11.1. GANs in medicine
11.2. GANs in fashion
11.3. Conclusion
Summary
Chapter 12. Looking ahead
12.1. Ethics
12.2. GAN innovations
12.3. Further reading
12.4. Looking back and closing thoughts
Summary
Training Generative Adversarial Networks (GANs)
Index
List of Figures
List of Tables
List of Listings
