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International Conference on Artificial Neural Networks

ICANN 2019: Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions pp 523–534Cite as

Capsule Networks for Attention Under Occlusion

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11731))

Abstract

Capsule Neural Networks (CapsNet) serve as an attempt to model the neural organization in biological neural networks. Through the routing-by-agreement algorithm, the attention mechanism is implemented as individual capsules that focus on specific upstream capsules while ignoring the rest. By using the routing algorithm, CapsNets are able to attend overlapping digits from the MNIST dataset. In this work, we evaluate the attention capabilities of Capsule Networks using the routing-by-agreement with occluded shape stimuli as presented in neurophysiology. We do so by implementing a more compact type of capsule network. Our results in classifying the non-occluded as well as the occluded shapes show that indeed, CapsNets are able to differentiate occlusions from near-occlusion situations as in real biological neurons. In our experiments, performing the reconstruction of the occluded stimuli also shows promising results.

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Acknowledgments

We would like to thank Sebastian Stabinger for his useful comments and Prof. Anitha Pasupathy for providing the program to create the single shape stimuli.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Innsbruck, Innsbruck, Austria

    Antonio Rodríguez-Sánchez & Tobias Dick

Authors
  1. Antonio Rodríguez-Sánchez
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  2. Tobias Dick
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Corresponding author

Correspondence to Antonio Rodríguez-Sánchez .

Editor information

Editors and Affiliations

  1. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Igor V. Tetko

  2. Institute of Computer Science, Czech Academy of Sciences, Prague 8, Czech Republic

    Věra Kůrková

  3. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Pavel Karpov

  4. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Fabian Theis

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Rodríguez-Sánchez, A., Dick, T. (2019). Capsule Networks for Attention Under Occlusion. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions. ICANN 2019. Lecture Notes in Computer Science(), vol 11731. Springer, Cham. https://doi.org/10.1007/978-3-030-30493-5_50

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  • DOI: https://doi.org/10.1007/978-3-030-30493-5_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30492-8

  • Online ISBN: 978-3-030-30493-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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