Structural Teacher-Student Normality Learning for Multi-Class Anomaly Detection and Localization
CoRR(2024)
Abstract
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
anomaly detection and localization tasks, respectively. Furthermore, our
algorithm outperforms the current state-of-the-art unified models on both
MVTecAD and VisA.
MoreTranslated text
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined