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Advanced Deep Learning with Keras

You're reading from   Advanced Deep Learning with Keras Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more

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Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781788629416
Length 368 pages
Edition 1st Edition
Languages
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Author (1):
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Rowel Atienza Rowel Atienza
Author Profile Icon Rowel Atienza
Rowel Atienza
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Table of Contents (13) Chapters Close

Preface 1. Introducing Advanced Deep Learning with Keras FREE CHAPTER 2. Deep Neural Networks 3. Autoencoders 4. Generative Adversarial Networks (GANs) 5. Improved GANs 6. Disentangled Representation GANs 7. Cross-Domain GANs 8. Variational Autoencoders (VAEs) 9. Deep Reinforcement Learning 10. Policy Gradient Methods Other Books You May Enjoy Index

Principles of CycleGAN

Principles of CycleGAN

Figure 7.1.1: Example of aligned image pair: left, original image and right, transformed image using a Canny edge detector. Original photos were taken by the author.

Translating an image from one domain to another is a common task in computer vision, computer graphics, and image processing. The preceding figure shows edge detection which is a common image translation task. In this example, we can consider the real photo (left) as an image in the source domain and the edge detected photo (right) as a sample in the target domain. There are many other cross-domain translation procedures that have practical applications such as:

  • Satellite image to map
  • Face image to emoji, caricature or anime
  • Body image to the avatar
  • Colorization of grayscale photos
  • Medical scan to a real photo
  • Real photo to an artist's painting

There are many more examples of this in different fields. In computer vision and image processing, for example, we can perform the translation by inventing an algorithm...

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