Xingshuang Luo

Filter or Compensate: Towards Invariant Representation from Distribution Shift for Anomaly Detection

Recent Anomaly Detection (AD) methods have achieved great success with In-Distribution (ID) data. However, real-world data often exhibits distribution shift, causing huge performance decay on traditional AD methods. From this perspective, few previous work has explored AD with distribution shift, an…