Convolutional Restricted Boltzmann Machines for Feature Learning

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منو

" 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)