Learning Unified Reference Representation for Unsupervised Multi-class
Anomaly Detection
Learning Unified Reference Representation for Unsupervised Multi-class
Anomaly Detection
In the field of multi-class anomaly detection, reconstruction-based methods derived from single-class anomaly detection face the well-known challenge of ``learning shortcuts'', wherein the model fails to learn the patterns of normal samples as it should, opting instead for shortcuts such as identity mapping or artificial noise elimination. Consequently, the model …