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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

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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