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Simplifying ConvNets for Fast Learning

  • Franck Mamalet
  • Christophe Garcia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7553)

Abstract

In this paper, we propose different strategies for simplifying filters, used as feature extractors, to be learnt in convolutional neural networks (ConvNets) in order to modify the hypothesis space, and to speed-up learning and processing times. We study two kinds of filters that are known to be computationally efficient in feed-forward processing: fused convolution/sub-sampling filters, and separable filters. We compare the complexity of the back-propagation algorithm on ConvNets based on these different kinds of filters. We show that using these filters allows to reach the same level of recognition performance as with classical ConvNets for handwritten digit recognition, up to 3.3 times faster.

Keywords

Speedup Factor Convolutional Neural Network Kernel Size Hypothesis Space Handwritten Digit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Franck Mamalet
    • 1
  • Christophe Garcia
    • 2
  1. 1.Orange LabsCesson-SévignéFrance
  2. 2.LIRIS, CNRS, Insa de LyonVilleurbanneFrance

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