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Mobile Computing, Applications, and Services. 12th EAI International Conference, MobiCASE 2021, Virtual Event, November 13–14, 2021, Proceedings

Research Article

When Neural Networks Using Different Sensors Create Similar Features

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  • @INPROCEEDINGS{10.1007/978-3-030-99203-3_5,
        author={Hugues Moreau and Andr\^{e}a Vassilev and Liming Chen},
        title={When Neural Networks Using Different Sensors Create Similar Features},
        proceedings={Mobile Computing, Applications, and Services. 12th EAI International Conference, MobiCASE 2021, Virtual Event, November 13--14, 2021, Proceedings},
        proceedings_a={MOBICASE},
        year={2022},
        month={3},
        keywords={Multimodal sensors Deep learning Transport Mode detection Inertial sensors Canonical Correlation Analysis},
        doi={10.1007/978-3-030-99203-3_5}
    }
    
  • Hugues Moreau
    Andréa Vassilev
    Liming Chen
    Year: 2022
    When Neural Networks Using Different Sensors Create Similar Features
    MOBICASE
    Springer
    DOI: 10.1007/978-3-030-99203-3_5
Hugues Moreau1,*, Andréa Vassilev1, Liming Chen2
  • 1: Université Grenoble Alpes, CEA, Leti
  • 2: Department of Mathematics and Computer Science, Ecole Centrale de Lyon
*Contact email: hugues.moreau@cea.fr

Abstract

Multimodal problems are omnipresent in the real world: autonomous driving, robotic grasping, scene understanding, etc... Instead of proposing to improve an existing method or algorithm: we will use existing statistical methods to understand the features in already-existing neural networks. More precisely, we demonstrate that a fusion method relying on Canonical Correlation Analysis on features extracted from Deep Neural Networks using different sensors is equivalent to looking at the output of the networks themselves.

Keywords
Multimodal sensors Deep learning Transport Mode detection Inertial sensors Canonical Correlation Analysis
Published
2022-03-24
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-99203-3_5
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