Xplique: A Deep Learning Explainability Toolbox

Today's most advanced machine-learning models are hardly scrutable. The key challenge for explainability methods is to help assisting researchers in opening up these black boxes, by revealing the strategy that led to a given decision, by characterizing their internal states or by studying the u...

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Bibliographic Details
Published in:arXiv.org 2022-06
Main Authors: Fel, Thomas, Lucas Hervier, Vigouroux, David, Poche, Antonin, Plakoo, Justin, Cadene, Remi, Chalvidal, Mathieu, Julien, Colin, Boissin, Thibaut, Bethune, Louis, Picard, Agustin, Nicodeme, Claire, Gardes, Laurent, Flandin, Gregory, Serre, Thomas
Format: Article
Language:English
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Deep learning
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Machine learning
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Summary:Today's most advanced machine-learning models are hardly scrutable. The key challenge for explainability methods is to help assisting researchers in opening up these black boxes, by revealing the strategy that led to a given decision, by characterizing their internal states or by studying the underlying data representation. To address this challenge, we have developed Xplique: a software library for explainability which includes representative explainability methods as well as associated evaluation metrics. It interfaces with one of the most popular learning libraries: Tensorflow as well as other libraries including PyTorch, scikit-learn and Theano. The code is licensed under the MIT license and is freely available at github.com/deel-ai/xplique.
ISSN:2331-8422