A Recover-then-Discriminate Framework for Robust Anomaly Detection
A Recover-then-Discriminate Framework for Robust Anomaly Detection
Anomaly detection (AD) has been extensively studied and applied in a wide range of scenarios in the recent past. However, there are still gaps between achieved and desirable levels of recognition accuracy for making AD for practical applications. In this paper, we start from an insightful analysis of two types …