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Generative Adversarial Networks Projects

You're reading from   Generative Adversarial Networks Projects Build next-generation generative models using TensorFlow and Keras

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781789136678
Length 316 pages
Edition 1st Edition
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Author (1):
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Kailash Ahirwar Kailash Ahirwar
Author Profile Icon Kailash Ahirwar
Kailash Ahirwar
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Table of Contents (11) Chapters Close

Preface 1. Introduction to Generative Adversarial Networks FREE CHAPTER 2. 3D-GAN - Generating Shapes Using GANs 3. Face Aging Using Conditional GAN 4. Generating Anime Characters Using DCGANs 5. Using SRGANs to Generate Photo-Realistic Images 6. StackGAN - Text to Photo-Realistic Image Synthesis 7. CycleGAN - Turn Paintings into Photos 8. Conditional GAN - Image-to-Image Translation Using Conditional Adversarial Networks 9. Predicting the Future of GANs 10. Other Books You May Enjoy

Introducing cGANs for face aging

So far, we have implemented different GAN networks for different use cases. Conditional GANs extend the idea of vanilla GANs and allow us to control the output of the generator network. Face aging is all about changing the age of a person's face without changing their identity. In most other models (including GANs), the appearance or identity of a person is lost by 50% because facial expressions and facial accessories, such as sunglasses or beards, are not taken into account. Age-cGANs consider all of these attributes. In this section, we will explore cGANs for face aging.

Understanding cGANs

cGANs are a type of GAN that are conditioned on some extra information. We feed the extra information...

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