Faster CNNs with Direct Sparse Convolutions and Guided Pruning
Abstract
Phenomenally successful in practical inference problems, convolutional neural networks (CNN) are widely deployed in mobile devices, data centers, and even supercomputers. The number of parameters needed in CNNs, however, are often large and undesirable. Consequently, various methods have been developed to prune a CNN once it is trained. Nevertheless, the resulting CNNs offer limited benefits. While pruning the fully connected layers reduces a...
Paper Details
Title
Faster CNNs with Direct Sparse Convolutions and Guided Pruning
Published Date
Aug 4, 2016
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