Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups

  title={Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups},
  author={Yani Andrew Ioannou and Duncan P. Robertson and Roberto Cipolla and Antonio Criminisi},
  journal={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
We propose a new method for creating computationally efficient and compact convolutional neural networks (CNNs) using a novel sparse connection structure that resembles a tree root. [] Key Result Our results show similar or higher accuracy than the baseline architectures with much less computation, as measured by CPU and GPU timings. For example, for ResNet 50, our model has 40% fewer parameters, 45% fewer floating point operations, and is 31% (12%) faster on a CPU (GPU).

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