Kailun Liao

Multi-Prototype Compactness and Boundary-Aware Synthesis for Unsupervised Anomaly Detection

Unsupervised Anomaly Detection (UAD) is crucial for industrial quality control. Many existing embedding-based methods adopt a single-prototype assumption and learn, for example, a compact hypersphere to enclose all normal features. However, this strategy breaks down under intra-class variance caused