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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

Product type Paperback

Published in Feb 2020

Publisher Packt

ISBN-13 9781838821654

Length 512 pages

Edition 2nd Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Author (1):

Rowel Atienza

View More author details

Table of Contents (16) Chapters

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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Authors (1)

Rowel Atienza

Rowel Atienza

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).

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