رکورد قبلیرکورد بعدی

" Convolutional Restricted Boltzmann Machines for Feature Learning "


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)
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MS23461_10498.pdf
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