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"
Convolutional Restricted Boltzmann Machines for Feature Learning
"
Mohammad Norouzi
A. A. Bulatov
Document Type
:
Latin Dissertation
Language of Document
:
English
Record Number
:
53421
Doc. No
:
TL23375
Call number
:
MS23461
Main Entry
:
Mohammad Norouzi
Title & Author
:
Convolutional Restricted Boltzmann Machines for Feature Learning\ Mohammad Norouzi
College
:
Simon Fraser University (Canada)
Date
:
2009
Degree
:
M.S.
student score
:
2009
Page No
:
63
Abstract
:
In this thesis, we present a probabilistic generative approach for learning hierarchical struc- tures of spatially local features, effective for visual recognition. Recently, a greedy layerwise learning mechanism has been proposed for training fully-connected neural networks. This mechanism views each of the network’s layers as a Restricted Boltzmann Machines (RBM), and trains them separately and bottom-up. We develop Convolutional RBM (CRBM), in which connections are local and weights are shared to respect the spatial structure of images. We train a hierarchy of visual feature detectors in layerwise manner by switching between the CRBM models and down-sampling layers. Our model learns generic gradient features at the bottom layers and class-specific features in the top levels. It is experimentally demon- strated that the features automatically learned by our algorithm are effective for visual recognition tasks, by using them to obtain performance comparable to the state-of-the-art on handwritten digit classification and pedestrian detection.
Subject
:
Applied sciences; Computer science; 0984:Computer science
Added Entry
:
A. A. Bulatov
Added Entry
:
Simon Fraser University (Canada)
https://lib.clisel.com/site/catalogue/53421
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MS23461_10498.pdf
MS23461.pdf
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