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Advanced Deep Learning with Keras

You're reading from  Advanced Deep Learning with Keras

Product type Book
Published in Oct 2018
Publisher Packt
ISBN-13 9781788629416
Pages 368 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Rowel Atienza Rowel Atienza
Profile icon Rowel Atienza
LinkedIn
Rowel Atienza is an Associate Professor at the Electrical and Electronics Engineering Institute of the University of the Philippines, Diliman. He holds the Dado and Maria Banatao Institute Professorial Chair in Artificial Intelligence. Rowel has been fascinated with intelligent robots since he graduated from the University of the Philippines. He received his MEng from the National University of Singapore for his work on an AI-enhanced four-legged robot. He finished his Ph.D. at The Australian National University for his contribution on the field of active gaze tracking for human-robot interaction. Rowel's current research work focuses on AI and computer vision. He dreams on building useful machines that can perceive, understand, and reason. To help make his dreams become real, Rowel has been supported by grants from the Department of Science and Technology (DOST), Samsung Research Philippines, and Commission on Higher Education-Philippine California Advanced Research Institutes (CHED-PCARI).
See other products by Rowel Atienza

Table of Contents (13) Chapters

Preface 1. Introducing Advanced Deep Learning with Keras 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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Authors (1)

Profile icon Rowel Atienza
LinkedIn
Rowel Atienza is an Associate Professor at the Electrical and Electronics Engineering Institute of the University of the Philippines, Diliman. He holds the Dado and Maria Banatao Institute Professorial Chair in Artificial Intelligence. Rowel has been fascinated with intelligent robots since he graduated from the University of the Philippines. He received his MEng from the National University of Singapore for his work on an AI-enhanced four-legged robot. He finished his Ph.D. at The Australian National University for his contribution on the field of active gaze tracking for human-robot interaction. Rowel's current research work focuses on AI and computer vision. He dreams on building useful machines that can perceive, understand, and reason. To help make his dreams become real, Rowel has been supported by grants from the Department of Science and Technology (DOST), Samsung Research Philippines, and Commission on Higher Education-Philippine California Advanced Research Institutes (CHED-PCARI).
Read more
See other products by Rowel Atienza

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