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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

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

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Index

-VAE: VAE with disentangled latent representations

In Chapter 6, Disentangled Representation GANs, the concept, and importance of the disentangled representation of latent codes were discussed. We can recall that a disentangled representation is where single latent units are sensitive to changes in single generative factors while being relatively invariant to changes in other factors [3]. Varying a latent code results to changes in one attribute of the generated output while the rest of the properties remain the same.

In the same chapter, InfoGANs [4] demonstrated to us that in the MNIST dataset, it is possible to control which digit to generate and the tilt and thickness of writing style. Observing the results in the previous section, it can be noticed that the VAE is intrinsically disentangling the latent vector dimensions to a certain extent. For example, looking at digit 8 in Figure 8.2.6, navigating z[1] from top to bottom decreases the width and roundness...

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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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