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Authors: Adrien Halnaut ; Romain Giot ; Romain Bourqui and David Auber

Affiliation: Univ. Bordeaux, Bordeaux INP, CNRS, LaBRI, UMR5800, F-33400 Talence, France

Keyword(s): Deep Neural Network, Explainable Machine Learning, Sankey Diagram, Parallel Coordinates.

Abstract: Deep neural networks are becoming omnipresent in reason of their growing popularity in media and their daily use. However, their global complexity makes them hard to understand which emphasizes their black-box aspect and the lack of confidence given by their potential users. The use of tailored visual and interactive representations is one way to improve their explainability and trustworthy. Inspired by parallel coordinates and Sankey diagrams, this paper proposes a novel visual representation allowing tracing the progressive classification of a trained classification neural network by examining how each evaluation data is being processed by each network’s layer. It is thus possible to observe which data classes are quickly recognized, unstable, or lately recognized. Such information provides insights to the user about the model architecture’s pertinence and can guide on its improvement. The method has been validated on two classification neural networks inspired from the literature (LeNet5 and VGG16) using two public databases (MNIST and FashionMNIST). (More)

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Paper citation in several formats:
Halnaut, A.; Giot, R.; Bourqui, R. and Auber, D. (2020). Deep Dive into Deep Neural Networks with Flows. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - IVAPP; ISBN 978-989-758-402-2; ISSN 2184-4321, SciTePress, pages 231-239. DOI: 10.5220/0008989702310239

@conference{ivapp20,
author={Adrien Halnaut. and Romain Giot. and Romain Bourqui. and David Auber.},
title={Deep Dive into Deep Neural Networks with Flows},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - IVAPP},
year={2020},
pages={231-239},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008989702310239},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - IVAPP
TI - Deep Dive into Deep Neural Networks with Flows
SN - 978-989-758-402-2
IS - 2184-4321
AU - Halnaut, A.
AU - Giot, R.
AU - Bourqui, R.
AU - Auber, D.
PY - 2020
SP - 231
EP - 239
DO - 10.5220/0008989702310239
PB - SciTePress

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