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Examination of the Deep Neural Networks in Classification of Distorted Signals

  • Michał KoziarskiEmail author
  • Bogusław Cyganek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9693)

Abstract

Classification of distorted patterns poses real problem for majority of classifiers. In this paper we analyse robustness of deep neural network in classification of such patterns. Using specific convolutional network architecture, an impact of different types of noise on classification accuracy is evaluated. For highly distorted patterns to improve accuracy we propose a preprocessing method of input patterns. Finally, an influence of different types of noise on classification accuracy is also analysed.

Keywords

Noise Image recognition Convolutional neural networks 

Notes

Acknowledgement

This work was supported by the Polish National Science Centre under the grant no. DEC-2014/15/B/ST6/00609.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Wrocław University of TechnologyWrocławPoland
  2. 2.AGH University of Science and TechnologyKrakówPoland

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