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GANs in Action: Deep learning with Generative Adversarial Networks
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GANs in Action: Deep learning with Generative Adversarial Networks
by Jakub Langr, Vladimir Bok
GANs in Action
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
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Copyright
GANs in Action: Deep learning with Generative Adversarial Networks
Jakub Langr and Vladimir Bok
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