Xplique: A Deep Learning Explainability Toolbox

2022/06/09 by Fel, Thomas, Hervier, Lucas, Vigouroux, David +12 · 3 citations
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · doi:10.48550/arxiv.2206.04394

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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• Back to the Baseline: Examining Baseline Effects on Explainability Metrics
• Interpreto: An Explainability Library for Transformers
• A Survey on Interpretability in Visual Recognition

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