Few-class Fidelity: Evaluating Explanations of Real-conditions CNN classifiers with Optimized Perturbations
This paper proposes 'Few-class Fidelity,' a variation of fidelity-based XAI metrics for real-world CNN classifiers with few classes. It uses optimized perturbations to measure faithfulness and compares with human-centric metrics on medical and natural images, revealing domain-data-XAI correlations.
[2606.28391] Few-class Fidelity: Evaluating Explanations of Real-conditions CNN classifiers with Optimized Perturbations
[Submitted on 23 Jun 2026]
Title:Few-class Fidelity: Evaluating Explanations of Real-conditions CNN classifiers with Optimized Perturbations
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Abstract:The wide use of Convolutional Neural Networks (CNN) in numerous domains and real-world classification applications is justified by their high precision and automation speed, helping users concentrate on higher-expertise tasks. To better understand the models and avoid bias during deployment, eXplainable Artificial Intelligence (XAI) techniques can be used after training. But as the list of XAI solutions expand, comparisons between them diverge, and consensus over their evaluation cannot be reached. This paper proposes a variation of Fidelity-based XAI metrics, with a focus on real-conditions applications, where the number of classes is often low. The approach generates in-distribution, uncertainty-provoking perturbations, to ensure proper measurement of the XAI methods faithfulness. As demonstration of the evaluation framework usefulness, it is compared with human-centric object localization and segmentation metrics. Once applied to both medical and natural imaging applications, it highlights the intricate correlation between domain, data curation, and XAI solution choices in order to validate training of a new CNN model.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.28391 [cs.CV]
(or arXiv:2606.28391v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.28391
arXiv-issued DOI via DataCite
Submission history
From: Wistan Marchadour [view email] [v1] Tue, 23 Jun 2026 17:26:47 UTC (6,402 KB)
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