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Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition

You're reading from Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition

Product type Book

Published in Feb 2020

Publisher Packt

ISBN-13 9781838821654

Pages 512 pages

Edition 2nd Edition

Languages

Concepts

Programming Language

Author (1):

Rowel Atienza

Table of Contents (16) 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

11. Object Detection

12. Semantic Segmentation

13. Unsupervised Learning Using Mutual Information

14. Other Books You May Enjoy

15. Index

Cross-Domain GANs

In computer vision, computer graphics, and image processing, a number of tasks involve translating an image from one form to another. The colorization of grayscale images, converting satellite images to maps, changing the artwork style of one artist to another, making night-time images into daytime, and summer photos to winter, are just a few examples. These tasks are referred to as cross-domain transfer and will be the focus of this chapter. An image in the source domain is transferred to a target domain, resulting in a new translated image.

A cross-domain transfer has a number of practical applications in the real world. As an example, in autonomous driving research, collecting road-scene driving data is both time-consuming and expensive. In order to cover as many scene variations as possible in that example, the roads would be traversed during different weather conditions, seasons, and times, giving us a large and varied amount of data. With the...

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Authors (1)

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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