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Authors: Felizia Quetscher ; Christof Kaufmann and Jörg Frochte

Affiliation: Bochum University of Applied Science, 42579 Heiligenhaus, Germany

Keyword(s): Explainability, Capsule Networks, Dynamic Routing, Classification, Image Recognition.

Abstract: Explainable Artificial Intelligence (AI) is a long-ranged goal, which can be approached from different viewpoints. One way is to simplify the complex AI model into an explainable one, another way uses post- processing to highlight the most important input features for the classification. In this work, we focus on the explanation of image classification using capsule networks with dynamic routing. We train a capsule network on the EMNIST letter dataset and examine the model regarding its explanatory potential. We show that the length of the class specific vectors (squash vectors) of the capsule network can be interpreted as predicted probability and it correlates with the agreement between the decoded image and the original image. We use the predicted probabilities to rank images within one class. By decoding different squash vectors, we visualize the interpretation of the image as the corresponding classes. Eventually, we create a set of modified letters to examine which features con tribute to the perception of letters. We conclude that this decoding of squash vectors provides a quantifiable tool towards explainability in AI applications. The explanations are trustworthy through the relation between the capsule network’s prediction and the corresponding visualization. (More)

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Paper citation in several formats:
Quetscher, F.; Kaufmann, C. and Frochte, J. (2022). Investigation of Capsule Networks Regarding their Potential of Explainability and Image Rankings. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 343-351. DOI: 10.5220/0010821600003116

@conference{icaart22,
author={Felizia Quetscher. and Christof Kaufmann. and Jörg Frochte.},
title={Investigation of Capsule Networks Regarding their Potential of Explainability and Image Rankings},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2022},
pages={343-351},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010821600003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Investigation of Capsule Networks Regarding their Potential of Explainability and Image Rankings
SN - 978-989-758-547-0
IS - 2184-433X
AU - Quetscher, F.
AU - Kaufmann, C.
AU - Frochte, J.
PY - 2022
SP - 343
EP - 351
DO - 10.5220/0010821600003116
PB - SciTePress

- Science and Technology Publications, Lda.
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