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Restricted Boltzmann machine as an aggregation technique for binary descriptors


Szymon Sobczak, Rafal Kapela, Kevin McGuinness, Aleksandra Swietlicka, Dariusz Pazderski, Noel O'Connor

Publication Type: 
Refereed Original Article
The article presents a novel approach to the challenge of real-time image classification with deep neural networks. The proposed architecture of the neural network exploits computationally efficient local binary descriptors and uses a Restricted Boltzmann Machine (RBM) as a feature space projection step so that the resulting depth of the deep neural network can be reduced. A Contrastive Divergence procedure is used both for RBM training and for feature projection. The resulting neural networks exhibit performance close to the current state of the art but are characterized by a small model memory footprint (i.e., number of parameters) and extremely efficient computational complexity (i.e, response time). The low number of parameters makes these architectures applicable in embedded systems with limited memory or reduced computational capabilities.
Digital Object Identifer (DOI): 
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Publication Date: 
The Visual Computer
Research Group: 
Dublin City University (DCU)
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