Absolute-Unified Multi-Class Anomaly Detection via Class-Agnostic Distribution Alignment

The core message of this paper is to present a simple yet powerful method, Class-Agnostic Distribution Alignment (CADA), to address the challenge of multi-class anomaly detection without any class information. CADA aligns the mismatched anomaly score distributions of different classes to enable unified anomaly detection for all classes and samples.