Xplique: A Deep Learning Explainability Toolbox
- URL: http://arxiv.org/abs/2206.04394v1
- Date: Thu, 9 Jun 2022 10:16:07 GMT
- Title: Xplique: A Deep Learning Explainability Toolbox
- Authors: Thomas Fel, Lucas Hervier, David Vigouroux, Antonin Poche, Justin
Plakoo, Remi Cadene, Mathieu Chalvidal, Julien Colin, Thibaut Boissin, Louis
Bethune, Agustin Picard, Claire Nicodeme, Laurent Gardes, Gregory Flandin,
Thomas Serre
- Abstract summary: We have developed Xplique: a software library for explainability.
It includes representative explainability methods as well as associated evaluation metrics.
The code is licensed under the MIT license and is freely available.
- Score: 5.067377019157635
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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