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Advanced Deep Learning with TensorFlow 2 and Keras

You're reading from   Advanced Deep Learning with TensorFlow 2 and Keras Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781838821654
Length 512 pages
Edition 2nd Edition
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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 (16) Chapters Close

Preface 1. Introducing Advanced Deep Learning with Keras 2. Deep Neural Networks FREE CHAPTER 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 11. Object Detection 12. Semantic Segmentation 13. Unsupervised Learning Using Mutual Information 14. Other Books You May Enjoy
15. Index

1. Principles of CycleGAN

Translating an image from one domain to another is a common task in computer vision, computer graphics, and image processing. Figure 7.1.1 shows edge detection, which is a common image translation task:

Figure 7.1.1: Example of an aligned image pair: left, original image, and right, transformed image using a Canny edge detector. The original photo was taken by the author.

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 an 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...

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