CaBRNet, an open-source library for developing and evaluating Case-Based Reasoning Models

Romain Xu-Darme; Aymeric Varasse; Alban Grastien; Julien Girard; Zakaria Chihani

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arXiv · 2409.16693

CaBRNet, an open-source library for developing and evaluating Case-Based Reasoning Models

Romain Xu-Darme·Aymeric Varasse·Alban Grastien·Julien Girard·Zakaria Chihani

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Abstract

In the field of explainable AI, a vibrant effort is dedicated to the design of self-explainable models, as a more principled alternative to post-hoc methods that attempt to explain the decisions after a model opaquely makes them. However, this productive line of research suffers from common downsides: lack of reproducibility, unfeasible comparison, diverging standards. In this paper, we propose CaBRNet, an open-source, modular, backward-compatible framework for Case-Based Reasoning Networks: https://github.com/aiser-team/cabrnet.

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Romain Xu-Darme, Aymeric Varasse, Alban Grastien, Julien Girard, Zakaria Chihani. 2024-09-25. CaBRNet, an open-source library for developing and evaluating Case-Based Reasoning Models. https://arxiv.org/abs/2409.16693

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