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Does Pooling Really Matter? An Evaluation on Gait Recognition

  • Claudio Filipi Goncalves dos SantosEmail author
  • Thierry Pinheiro Moreira
  • Danilo ColomboEmail author
  • João Paulo PapaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11896)

Abstract

Most Convolutional Neural Networks make use of subsampling layers to reduce dimensionality and keep only the most essential information, besides turning the model more robust to rotation and translation variations. One of the most common sampling methods is the one who keeps only the maximum value in a given region, known as max-pooling. In this study, we provide pieces of evidence that, by removing this subsampling layer and changing the stride of the convolution layer, one can obtain comparable results but much faster. Results on the gait recognition task show the robustness of the proposed approach, as well as its statistical similarity to other pooling methods.

Keywords

Convolutional Neural Networks Deep learning Gait recognition 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Federal University of São Carlos - UFSCarSão CarlosBrazil
  2. 2.State University of Sao Paulo - UNESPSao PauloBrazil
  3. 3.Cenpes, Petróleo Brasileiro S.A. – PetrobrasRio de Janeiro - RJBrazil

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