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Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition

  • Dominik Scherer
  • Andreas Müller
  • Sven Behnke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6354)

Abstract

A common practice to gain invariant features in object recognition models is to aggregate multiple low-level features over a small neighborhood. However, the differences between those models makes a comparison of the properties of different aggregation functions hard. Our aim is to gain insight into different functions by directly comparing them on a fixed architecture for several common object recognition tasks. Empirical results show that a maximum pooling operation significantly outperforms subsampling operations. Despite their shift-invariant properties, overlapping pooling windows are no significant improvement over non-overlapping pooling windows. By applying this knowledge, we achieve state-of-the-art error rates of 4.57% on the NORB normalized-uniform dataset and 5.6% on the NORB jittered-cluttered dataset.

Keywords

Recognition Rate Window Function Convolutional Neural Network Convolutional Layer Test Error Rate 
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 2010

Authors and Affiliations

  • Dominik Scherer
    • 1
  • Andreas Müller
    • 1
  • Sven Behnke
    • 1
  1. 1.Institute of Computer Science VI, Autonomous Intelligent Systems GroupUniversity of BonnBonnGermany

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