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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

Practical applications of Age-cGAN

Age synthesis and age progression have many industrial and consumer applications:

  • Cross-age face recognition: This can be incorporated into security applications, such as mobile device unlocking or desktop unlocking. The problem with current face recognition systems is that they need to be updated with time. With Age-cGAN networks, the lifespan of cross-age face recognition systems will be longer.
  • Finding lost children: This is an interesting application of an Age-cGAN. As the age of a child increases, their facial features change, and it becomes much harder to identify them. An Age-cGAN can simulate a person's face at a specified age.
  • Entertainment: For example, in mobile applications, to show and share a friend's pictures at a specified age.
  • Visual effects in movies: It's a tedious and lengthy process to manually simulate...
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