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.
@article{arxiv.2409.16693,
title = {CaBRNet, an open-source library for developing and evaluating Case-Based Reasoning Models},
author = {Romain Xu-Darme and Aymeric Varasse and Alban Grastien and Julien Girard and Zakaria Chihani},
journal= {arXiv preprint arXiv:2409.16693},
year = {2024}
}