🤖 AI Summary
This work addresses the challenge in multi-class anomaly detection where unified models often suffer from anomaly replication and confusion among normal classes. To this end, we propose a label-free training framework that formulates the task as a representational capacity allocation problem. By leveraging a shared learnable prototype bank, our approach introduces a dual regularization mechanism—spatial prototype alignment and prototype-relative global alignment—to enhance reconstruction fidelity for normal samples and suppress anomaly replication, all without requiring class labels, negative samples, or memory-based retrieval. The method preserves the standard teacher–student feature discrepancy pipeline while significantly improving both the separation between anomaly and normal scores and the discriminability among normal categories. It achieves state-of-the-art average detection accuracies of 86.2%, 80.7%, and 73.1% on MVTec AD, VisA, and Real-IAD benchmarks, respectively.
📝 Abstract
Reconstruction-based anomaly detection is attractive for industrial inspection, but scaling it from category-specific training to a one-for-all setting is challenging. A single model must reconstruct diverse normal appearances without copying abnormal details, which exposes two coupled failure modes: identical shortcut, where anomalies pass through the reconstruction path, and mis-reconstruction, where normal categories are confused with one another. We propose \textbf{BoRAD}, a label-free training framework that treats this as a representation-capacity allocation problem. BoRAD uses a shared learnable prototype bank to impose two complementary regularizers: spatial prototype alignment contracts local within-prototype variation to suppress anomaly copying, while prototype-relative global alignment preserves between-prototype structure and improves sensitivity to abnormal angular deviations. The prototype bank and prediction heads are used only during training; inference remains a standard teacher-student feature discrepancy pass, with no class labels, negative pairs, memory retrieval, or prototype lookup. BoRAD achieves competitive one-for-all anomaly detection performance, including 86.2\% mAD on MVTec AD, 80.7\% mAD on VisA and 73.1\% mAD on Real-IAD. Diagnostic analyses further show reduced anomaly leakage, improved normal-category separability, and stronger anomaly-normal score separation.