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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 LearningMohammad 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)