@conference{EvaluatingPerformance_ExplanationMethods_2023,
author = "Javier Barbero-G{\'o}mez and Ricardo Cruz and Jaime S. Cardoso and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez",
abstract = "This paper introduces an evaluation procedure to validate the efficacy of explanation methods for Convolutional Neural Network (CNN) models in ordinal regression tasks. Two ordinal methods are contrasted against a baseline using cross-entropy, across four datasets. A statistical analysis demonstrates that attribution methods, such as Grad-CAM and IBA, perform significantly better when used with ordinal regression CNN models compared to a baseline approach in most ordinal and nominal metrics. The study suggests that incorporating ordinal information into the attribution map construction process may improve the explanations further.",
booktitle = "IWANN 2023: Advances in Computational Intelligence",
doi = "10.1007/978-3-031-43078-7_43",
keywords = "Convolutional Neural Networks, Interpretability, Ordinal Regression",
number = "1",
pages = "1--12",
title = "{E}valuating the {P}erformance of {E}xplanation {M}ethods on {O}rdinal {R}egression {CNN} {M}odels",
url = "link.springer.com/chapter/10.1007/978-3-031-43078-7_43",
year = "2023",
}