Michael Morin

Alumni

Publications

Toward Faithful Explanations in Acoustic Anomaly Detection

Maab Elrashid

Anthony Deschênes

Yusuf Cem Sübakan

Mirco Ravanaelli

Rémi Georges

Michael Morin

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.

2026-01-18

ArXiv (prépublication)

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

2023-12-31

Computers in Industry (publié)