  Visual anomaly detection is a challenging open-set task aimed at identifying
unknown anomalous patterns while modeling normal data. The knowledge
distillation paradigm has shown remarkable performance in one-class anomaly
detection by leveraging teacher-student network feature comparisons. However,
extending this paradigm to multi-class anomaly detection introduces novel
scalability challenges. In this study, we address the significant performance
degradation observed in previous teacher-student models when applied to
multi-class anomaly detection, which we identify as resulting from cross-class
interference. To tackle this issue, we introduce a novel approach known as
Structural Teacher-Student Normality Learning (SNL): (1) We propose
spatial-channel distillation and intra-&inter-affinity distillation techniques
to measure structural distance between the teacher and student networks. (2) We
introduce a central residual aggregation module (CRAM) to encapsulate the
normal representation space of the student network. We evaluate our proposed
approach on two anomaly detection datasets, MVTecAD and VisA. Our method
surpasses the state-of-the-art distillation-based algorithms by a significant
margin of 3.9% and 1.5% on MVTecAD and 1.2% and 2.5% on VisA in the multi-class
anomaly detection and localization tasks, respectively. Furthermore, our
algorithm outperforms the current state-of-the-art unified models on both
MVTecAD and VisA.
