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Erschienen in:
A Novel Approach for Mobile Robot Localization in Topological Maps Using Classification with Reject Option from Structural Co-occurrence Matrix Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Deep Boltzmann Machines Using Adaptive Temperatures

verfasst von : Leandro A. Passos Júnior, Kelton A. P. Costa, João P. Papa

Erschienen in: Computer Analysis of Images and Patterns

Verlag: Springer International Publishing

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Abstract

Deep learning has been considered a hallmark in a number of applications recently. Among those techniques, the ones based on Restricted Boltzmann Machines have attracted a considerable attention, since they are energy-driven models composed of latent variables that aim at learning the probability distribution of the input data. In a nutshell, the training procedure of such models concerns the minimization of the energy of each training sample in order to increase its probability. Therefore, such optimization process needs to be regularized in order to reach the best trade-off between exploitation and exploration. In this work, we propose an adaptive regularization approach based on temperatures, and we show its advantages considering Deep Belief Networks (DBNs) and Deep Boltzmann Machines (DBMs). The proposed approach is evaluated in the context of binary image reconstruction, thus outperforming temperature-fixed DBNs and DBMs.

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Vorheriges Kapitel On the Essence of Unsupervised Detection of Anomalous Motion in Surveillance Videos
Nächstes Kapitel Twin Deep Convolutional Neural Network for Example-Based Image Colorization
Fußnoten
2
The images are originally available in grayscale with resolution of \(28\times 28\), but they were reduced to \(14\times 14\) images.
 
3
The original training set was reduced to \(2\%\) of its former size, which corresponds to 1, 200 images.
 
5
Since this architecture has been commonly employed in several works in the literature, we opted to employ it in our work either.
 
6
One sampling iteration was used for all learning algorithms.
 
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Metadaten
Titel
Deep Boltzmann Machines Using Adaptive Temperatures
verfasst von
Leandro A. Passos Júnior
Kelton A. P. Costa
João P. Papa
Copyright-Jahr
2017
Buch
Computer Analysis of Images and Patterns

Print ISBN: 978-3-319-64688-6

Electronic ISBN: 978-3-319-64689-3

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-64689-3_14

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