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.
@article{arxiv.2206.04394,
title = {Xplique: A Deep Learning Explainability Toolbox},
author = {Thomas Fel and Lucas Hervier and David Vigouroux and Antonin Poche and Justin Plakoo and Remi Cadene and Mathieu Chalvidal and Julien Colin and Thibaut Boissin and Louis Bethune and Agustin Picard and Claire Nicodeme and Laurent Gardes and Gregory Flandin and Thomas Serre},
journal= {arXiv preprint arXiv:2206.04394},
year = {2022}
}