Interpretability is essential for user trust in real-world anomaly detection applications. However, deep learning models, despite their strong 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.

翻译：在现实世界的异常检测应用中，可解释性对于建立用户信任至关重要。然而，深度学习模型尽管性能强大，却常常缺乏透明度。在本工作中，我们通过比较标准自编码器与掩码自编码器在检测性能和可解释性方面的表现，研究了基于自编码器的音频异常检测模型的可解释性。我们应用了多种归因方法，包括误差图、显著图、SmoothGrad、积分梯度、GradSHAP 和 Grad-CAM。尽管 MAE 的检测性能略低，但它始终能提供更忠实且时间上更精确的解释，表明其与真实异常有更好的对齐。为了评估解释方法所突出区域的相关性，我们提出了一种基于扰动的忠实度度量，即用其重构来替换这些区域以模拟正常输入。基于真实工业场景的实验结果表明，我们的发现强调了将可解释性纳入异常检测流程的重要性，并表明掩码训练能在不损害性能的前提下提高解释质量。