Interpretability-Aware Industrial Anomaly Detection Using Autoencoders.

The past decade has witnessed wide applications of deep neural networks in anomaly detection. However, the dearth of interpretability in neural networks often hinders their reliability, especially for industrial applications where practical users heavily rely on interpretable methods to provide explanations for their decision-making. In this paper, we propose a reconstruction-based approach to unsupervised detection of anomalies in industrial defect data. Our algorithm employs an interpretability score during both the training and test phases. Specifically, we train an autoencoder with a loss function that incorporates an interpretability-aware error term. After training, the autoencoder processes a specific feature from the difference between the test image and the average of training images and produces an attention map that is used for detecting the anomalies. Our method not only achieves competitive performance compared with non-interpretability-aware methods but also produces attention maps that facilitate a direct explanation of detection results, which can potentially be useful for industrial practitioners.