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Convolutional Neural Networks Analysis Using Concentric-Rings Interactive Visualization

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Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1014))

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Abstract

The goal of this paper is to present the interactive web visualization technique DeepRings. The technique has a radial design, using concentric rings to represent the layers of a deep learning model, where each circular ring encodes the feature maps of that layer. The proposed technique allows to perform analysis of tasks over time regarding a single model or a comparison between two distinct models, thus contributing to a better understanding of the behavior of such models. The design supports several training methods designed to solve Computer Vision tasks, like supervised learning and self-supervised learning, as well as reinforcement learning. Additional charts highlight similarity metrics, and interaction techniques such as filtering help reduce the analysis data. Finally, preliminary evaluations were conducted with domain experts highlighting positive points and aspects that can be improved, suggesting avenues for future work.

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Notes

  1. 1.

    GDPR Legal Text—https://eur-lex.europa.eu/eli/reg/2016/679/oj.

  2. 2.

    D3 Website—https://d3js.org/ (Accessed: 28 December 2021).

  3. 3.

    TensorFlow Website—https://www.tensorflow.org/ (Accessed: 28 December 2021).

  4. 4.

    Flask Website—https://flask.palletsprojects.com (Accessed: 28 December 2021).

  5. 5.

    Unity Website—https://unity.com/.

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Acknowledgements

We thank everyone involved in discussion groups and case studies for their time and expertise. This research was developed in the scope of the Ph.D. grant [2020.05789.BD], funded by FCT—Foundation for Science and Technology. It was also supported by IEETA—Institute of Electronics and Informatics Engineering of Aveiro, funded by National Funds through FCT, in the context of the project [UID/CEC/00127/2019]. This study was also supported by PPGCC—UFPA—Computer Science Graduate Program of Federal University of Para, funded by National Funds through the CAPES Edital no 47/2017.

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Authors and Affiliations

  1. University of Aveiro, DETI-IEETA, Aveiro, Portugal

    João Alves, Tiago Araújo & Beatriz Sousa Santos

  2. Federal University of Pará, PPGCC, Belém, Brazil

    Tiago Araújo & Bianchi Serique Meiguins

Authors
  1. João Alves
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  2. Tiago Araújo
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  3. Bianchi Serique Meiguins
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  4. Beatriz Sousa Santos
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Corresponding author

Correspondence to João Alves .

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Editors and Affiliations

  1. Department of Computer Science, Central Washington University, Ellensburg, WA, USA

    Boris Kovalerchuk

  2. Department of Media, Darmstadt University of Applied Sciences, Darmstadt, Hessen, Germany

    Kawa Nazemi

  3. Department of Computer Science, Central Washington University, Ellensburg, WA, USA

    Răzvan Andonie

  4. Department of Electronics, Telecommunications and Computers Engineering, Lisbon School of Engineering, Lisbon, Portugal

    Nuno Datia

  5. Department of Informatics, London South Bank University, London, UK

    Ebad Banissi

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Alves, J., Araújo, T., Meiguins, B.S., Santos, B.S. (2022). Convolutional Neural Networks Analysis Using Concentric-Rings Interactive Visualization. In: Kovalerchuk, B., Nazemi, K., Andonie, R., Datia, N., Banissi, E. (eds) Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery. Studies in Computational Intelligence, vol 1014. Springer, Cham. https://doi.org/10.1007/978-3-030-93119-3_6

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