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  4. Generalization of SELU to CNN
 
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2019
Master Thesis
Title

Generalization of SELU to CNN

Abstract
Neural network based object detectors are able to automatize many difficult, tedious tasks. However, they are usually slow and/or require powerful hardware. One main reason is called Batch Normalization (BN) [1], which is an important method for building these detectors. Recent studies present a potential replacement called Self-normalizing Neural Network (SNN) [2], which at its core is a special activation function named Scaled Exponential Linear Unit (SELU). This replacement seems to have most of BNs benefits while requiring less computational power. Nonetheless, it is uncertain that SELU and neural network based detectors are compatible with one another. An evaluation of SELU incorporated networks would help clarify that uncertainty. Such evaluation is performed through series of tests on different neural networks. After the evaluation, it is concluded that, while indeed faster, SELU is still not as good as BN for building complex object detector networks.
Thesis Note
Sankt Augustin, Hochschule Bonn-Rhein-Sieg, Master Thesis, 2019
Author(s)
Ha, Bach  orcid-logo
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Person Involved
Plöger, Paul G.
Kraetzschmar, Gerhard K.
Zimmermann, Florian
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Publishing Place
Sankt Augustin
File(s)
Download (1.5 MB)
Rights
Use according to copyright law
DOI
10.24406/publica-fhg-282624
Language
English
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Keyword(s)
  • deep learning

  • object detection

  • Batch Normalization

  • SELU

  • YOLO v3

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