Multi-Prototype Compactness and Boundary-Aware Synthesis for Unsupervised Anomaly Detection
Liao, Kailun, Yang, Jianfeng, Tao, Tao, Wu, Wenfei, Jiang, Jiaming, Xiao, Jinsheng
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 by changes in illumination, pose, or texture. To cover all diverse normal samples, a single prototype may induce an overly loose decision boundary, making subtle anomalies hard to detect. To overcome this limitation, we propose PGBL (Prototype-Guided Boundary Learning), a framework that synergizes structured representation learning with targeted anomaly synthesis. First, the Multi-Prototype Compact Constraint (MPCC) module models the normal feature distribution as a mixture of multiple semantic prototypes, enabling tighter local representations for each normal sub-pattern. Second, instead of blind anomaly synthesis, the Boundary-Aware Anomaly Synthesis (BAAS) module generates pseudo-anomalies at the topological boundaries between MPCC clusters. Finally, a Discriminative Boundary Refiner (DBR) learns to shape the final decision surface by separating normal clusters from the synthesized anomalies. Extensive experiments on MVTec-AD, VisA and Real-IAD show that PGBL consistently outperforms prior methods in both anomaly detection and localization.
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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表