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Deep neural networks are a powerful tool for feature learning and extractiongiven their ability to model high-level abstractions in highly complex data.One area worth exploring in feature learning and extraction using deep neuralnetworks is efficient neural connectivity formation for faster feature learningand extraction. Motivated by findings of stochastic synaptic connectivityformation in the brain as well as the brain's uncanny ability to efficientlyrepresent information, we propose the efficient learning and extraction offeatures via StochasticNets, where sparsely-connected deep neural networks canbe formed via stochastic connectivity between neurons. To evaluate thefeasibility of such a deep neural network architecture for feature learning andextraction, we train deep convolutional StochasticNets to learn abstractfeatures using the CIFAR-10 dataset, and extract the learned features fromimages to perform classification on the SVHN and STL-10 datasets. Experimentalresults show that features learned using deep convolutional StochasticNets,with fewer neural connections than conventional deep convolutional neuralnetworks, can allow for better or comparable classification accuracy thanconventional deep neural networks: relative test error decrease of ~4.5% forclassification on the STL-10 dataset and ~1% for classification on the SVHNdataset. Furthermore, it was shown that the deep features extracted using deepconvolutional StochasticNets can provide comparable classification accuracyeven when only 10% of the training data is used for feature learning. Finally,it was also shown that significant gains in feature extraction speed can beachieved in embedded applications using StochasticNets. As such, StochasticNetsallow for faster feature learning and extraction performance while facilitatefor better or comparable accuracy performances.

Efficient Deep Feature Learning and Extraction via StochasticNets