Remembering Normality: Memory-guided Knowledge Distillation for Unsupervised Anomaly Detection

Abstract

Knowledge distillation (KD) has been widely explored in unsupervised anomaly detection (AD). The student is assumed to constantly produce representations of typical patterns within trained data, named "normality", and the representation discrepancy between the teacher and student model is identified as anomalies. However, it suffers from the "normality forgetting" issue. Trained on anomaly-free data, the student still well reconstructs anomalous representations for anomalies and is sensitive to fine patterns in normal data, which also appear in training. To mitigate this issue, we introduce a novel Memory-guided Knowledge-Distillation (MemKD) framework that adaptively modulates the normality of student features in detecting anomalies. Specifically, we first propose a normality recall memory (NR Memory) to strengthen the normality of student-generated features by recalling the stored normal information. In this sense, representations will not present anomalies and fine patterns will be well described. Subsequently, we employ a normality embedding learning strategy to promote information learning for the NR Memory. It constructs a normal exemplar set so that the NR Memory can memorize prior knowledge in anomaly-free data and later recall them from the query feature. Consequently, comprehensive experiments demonstrate that the proposed MemKD achieves promising results on five benchmarks.