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Neural network training fingerprint: visual analytics of the training process in classification neural networks

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Abstract

The striking results of deep neural networks (DNN) have motivated its wide acceptance to tackle large datasets and complex tasks such as natural language processing, facial recognition, and artificial image generation. However, DNN parameters are often empirically selected on a trial-and-error approach without detailed information on convergence behavior. While some visualization techniques have been proposed to aid the comprehension of general-purpose neural networks, only a few explore the training process, lacking the ability to adequately display how abstract representations are formed and represent the influence of training parameters during this process. This paper describes neural network training fingerprint (NNTF), a visual analytics approach to investigate the training process of any neural network performing classification. NNTF allows understanding how classification decisions change along the training process, displaying information about convergence, oscillations, and training rates. We show its usefulness through case studies and demonstrate how it can support the analysis of training parameters.

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Notes

  1. Source code available at https://github.com/marthadais/NeuralNetworkTrainingFingerprint.

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Acknowledgements

We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC).

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Authors and Affiliations

  1. Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada

    Martha Dais Ferreira & Fernando V. Paulovich

  2. Department of Informatics, King’s College London, London, UK

    Gabriel D. Cantareira

  3. University of São Paulo, São Carlos, Brazil

    Rodrigo F. de Mello

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  1. Martha Dais Ferreira
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  2. Gabriel D. Cantareira
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  3. Rodrigo F. de Mello
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  4. Fernando V. Paulovich
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Correspondence to Martha Dais Ferreira.

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Ferreira, M.D., Cantareira, G.D., de Mello, R.F. et al. Neural network training fingerprint: visual analytics of the training process in classification neural networks. J Vis 25, 593–612 (2022). https://doi.org/10.1007/s12650-021-00809-4

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  • DOI: https://doi.org/10.1007/s12650-021-00809-4

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