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A Multi-layered Approach for Tailored Black-Box Explanations Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Samples Classification Analysis Across DNN Layers with Fractal Curves

Authors : Adrien Halnaut, Romain Giot, Romain Bourqui, David Auber

Published in: Pattern Recognition. ICPR International Workshops and Challenges

Publisher: Springer International Publishing

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Abstract

Deep Neural Networks are becoming the prominent solution when using machine learning models. However, they suffer from a black-box effect that renders complicated their inner workings interpretation and thus the understanding of their successes and failures. Information visualization is one way among others to help in their interpretability and hypothesis deduction. This paper presents a novel way to visualize a trained DNN to depict at the same time its architecture and its way of treating the classes of a test dataset at the layer level. In this way, it is possible to visually detect where the DNN starts to be able to discriminate the classes or where it could decrease its separation ability (and thus detect an oversized network). We have implemented the approach and validated it using several well-known datasets and networks. Results show the approach is promising and deserves further studies.

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previous chapter Expert Level Evaluations for Explainable AI (XAI) Methods in the Medical Domain
next chapter Random Forest Model and Sample Explainer for Non-experts in Machine Learning – Two Case Studies
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Metadata
Title
Samples Classification Analysis Across DNN Layers with Fractal Curves
Authors
Adrien Halnaut
Romain Giot
Romain Bourqui
David Auber
Copyright Year
2021
Book
Pattern Recognition. ICPR International Workshops and Challenges

Print ISBN: 978-3-030-68795-3

Electronic ISBN: 978-3-030-68796-0

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-68796-0_4

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