Anomaly detection is a crucial task that learns normal patterns from training samples to identify abnormal samples in test data. However, existing approaches often encounter challenges in real-world scenarios due to data drift resulting from external factors such as changes in camera angles, lighting conditions, and noise. In this work, we propose a robust anomaly detection model to deal with the domain shift. The key to our robustness to domain shift is to enhance the recall of out-of-distribution samples. First, we train a normality distillation student to fit diverse augmented normal patterns, in which we adopt a hard distillation loss and a structure distillation loss. Second, to enhance the accuracy of anomaly localization, we adopt a segmentation sub-network to integrate the outputs of teacher and student models. Experiments on the MvTec AD test set with random perturbations highlight the effectiveness of our method.
Comments