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Efficient Deep Feature Learning and Extraction via StochasticNets by Shafiee, Mohammad Javad; Siva, Parthipan; Fieguth, Paul; Wong, Alexander is a scholarly article available to read on EtoBox.

What is Efficient Deep Feature Learning and Extraction via StochasticNets about?

Deep neural networks are a powerful tool for feature learning and extraction given their ability to model high-level abstractions in highly complex data. One area worth exploring in feature learning and extraction using deep neural networks is efficient neural connectivity formation for faster feature learning and extraction. Motivated by findings of stochastic synaptic connectivity formation in the brain as well as the brain's uncanny ability to efficiently represent information, we propose the efficient learning and extraction of features via StochasticNets, where sparsely-connected deep neural networks can be formed via stochastic connectivity between neurons. To evaluate the feasibility of such a deep neural network architecture for feature learning and extraction, we train deep convolutional StochasticNets to learn abstract features using the CIFAR-10 dataset, and extract the learned features from images to perform classification on the SVHN and STL-10 datasets. Experimental results show that features learned using deep convolutional StochasticNets, with fewer neural connections than conventional deep convolutional neural networks, can allow for better or comparable classifica

Author

Shafiee, Mohammad Javad; Siva, Parthipan; Fieguth, Paul; Wong, Alexander

Published

2015

Language

EN