Multi Class Anomaly Detection

Multi-class anomaly detection is an advanced task that involves jointly learning and detecting anomalies across multiple categories, which, compared to traditional single-category anomaly detection, can more comprehensively identify abnormal situations in complex datasets. This task holds significant application value in the field of computer vision, capable of enhancing the robustness and accuracy of systems.