The Power of Sparsity in Convolutional Neural Networks

Soravit Changpinyo, Mark Sandler, Andrey Zhmoginov

Nov 04, 2016 (modified: Dec 03, 2016) ICLR 2017 conference submission readers: everyone
  • Abstract: Deep convolutional networks are well-known for their high computational and memory demands. Given limited resources, how does one design a network that balances its size, training time, and prediction accuracy? A surprisingly effective approach to trade accuracy for size and speed is to simply reduce the number of channels in each convolutional layer by a fixed fraction and retrain the network. In many cases this leads to significantly smaller networks with only minimal changes to accuracy. In this paper, we take a step further by empirically examining a strategy for deactivating connections between filters in convolutional layers in a way that allows us to harvest savings both in run-time and memory for many network architectures. More specifically, we generalize 2D convolution to use a channel-wise sparse connection structure and show that this leads to significantly better results than the baseline approach for large networks including VGG and Inception V3.
  • TL;DR: Sparse random connections that allow savings to be harvested and that are very effective at compressing CNNs.
  • Keywords: Deep learning, Supervised Learning
  • Conflicts: usc.edu, google.com

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