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Hands-On Generative Adversarial Networks with Keras

You're reading from   Hands-On Generative Adversarial Networks with Keras Your guide to implementing next-generation generative adversarial networks

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781789538205
Length 272 pages
Edition 1st Edition
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Author (1):
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Rafael Valle Rafael Valle
Author Profile Icon Rafael Valle
Rafael Valle
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Table of Contents (14) Chapters Close

Preface 1. Section 1: Introduction and Environment Setup FREE CHAPTER
2. Deep Learning Basics and Environment Setup 3. Introduction to Generative Models 4. Section 2: Training GANs
5. Implementing Your First GAN 6. Evaluating Your First GAN 7. Improving Your First GAN 8. Section 3: Application of GANs in Computer Vision, Natural Language Processing, and Audio
9. Progressive Growing of GANs 10. Generation of Discrete Sequences Using GANs 11. Text-to-Image Synthesis with GANs 12. TequilaGAN - Identifying GAN Samples 13. Whats next in GANs

References

[1] Martin Arjovsky and Léon Bottou. Towards principled methods for training generative adversarial networks. In NIPS 2016 Workshop on Adversarial Training. In review for ICLR, volume 2016, 2017.

[2] Martin Arjovsky, Soumith Chintala, and Léon Bottou. Wasserstein gan. arXiv preprint arXiv:1701.07875, 2017.

[3] Shane Barratt and Rishi Sharma. A note on the inception score. arXiv preprint arXiv:1801.01973, 2018.

[4] Olivier Breuleux, Yoshua Bengio, and Pascal Vincent. Quickly generating representative samples from an rbm-derived process. Neural Computation, 23(8):2058–2073, 2011.

[5] Wilson Cai, Anish Doshi, and Rafael Valle. Attacking speaker recognition with deep generative models. arXiv preprint arXiv:1801.02384, 2018.

[6] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio...

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