Michael Morin

Toward Faithful Explanations in Acoustic Anomaly Detection

Maab Elrashid

Rémi Georges

Interpretability is essential for user trust in real-world anomaly detection applications. However, deep learning models, despite their stro… (voir plus)ng performance, often lack transparency. In this work, we study the interpretability of autoencoder-based models for audio anomaly detection, by comparing a standard autoencoder (AE) with a mask autoencoder (MAE) in terms of detection performance and interpretability. We applied several attribution methods, including error maps, saliency maps, SmoothGrad, Integrated Gradients, GradSHAP, and Grad-CAM. Although MAE shows a slightly lower detection, it consistently provides more faithful and temporally precise explanations, suggesting a better alignment with true anomalies. To assess the relevance of the regions highlighted by the explanation method, we propose a perturbation-based faithfulness metric that replaces them with their reconstructions to simulate normal input. Our findings, based on experiments in a real industrial scenario, highlight the importance of incorporating interpretability into anomaly detection pipelines and show that masked training improves explanation quality without compromising performance.

2025-12-31

arXiv (publié)

doi.org

arxiv.org

Deep reinforcement learning for continuous wood drying production line control

François-Alexandre Tremblay

Audrey Durand

Michael Morin

Philippe Marier

Jonathan Gaudreault

2024-01-01

Computers in industry (Print) (publié)