  Training a unified model is considered to be more suitable for practical
industrial anomaly detection scenarios due to its generalization ability and
storage efficiency. However, this multi-class setting, which exclusively uses
normal data, overlooks the few but important accessible annotated anomalies in
the real world. To address the challenge of real-world anomaly detection, we
propose a new framework named Dual Memory bank enhanced representation learning
for Anomaly Detection (DMAD). This framework handles both unsupervised and
semi-supervised scenarios in a unified (multi-class) setting. DMAD employs a
dual memory bank to calculate feature distance and feature attention between
normal and abnormal patterns, thereby encapsulating knowledge about normal and
abnormal instances. This knowledge is then used to construct an enhanced
representation for anomaly score learning. We evaluated DMAD on the MVTec-AD
and VisA datasets. The results show that DMAD surpasses current
state-of-the-art methods, highlighting DMAD's capability in handling the
complexities of real-world anomaly detection scenarios.
