BoRAD: 自举表示实现多类异常检测
BoRAD: Bootstrap your Own Representations for Multi-class Anomaly Detection
AI总结：
提出BoRAD框架，通过原型正则化解决多类异常检测中重建模型的捷径和误重建问题，无需标签即可实现单模型多类检测。
摘要：
基于重建的异常检测在工业检测中具有吸引力，但将其从类别特定训练扩展到一劳永逸的设置具有挑战性。单个模型必须重建多样的正常外观，同时不复制异常细节，这暴露了两个耦合的失败模式：相同捷径，即异常通过重建路径；以及误重建，即正常类别相互混淆。我们提出\textbf{BoRAD}，一个无标签训练框架，将其视为表示容量分配问题。BoRAD使用共享的可学习原型库施加两个互补正则化器：空间原型对齐约束局部原型内变异以抑制异常复制，而原型相对全局对齐保留原型间结构并提高对异常角度偏差的敏感性。原型库和预测头仅在训练期间使用；推理保持标准的师生特征差异过程，无需类别标签、负样本对、内存检索或原型查找。BoRAD实现了具有竞争力的一劳永逸异常检测性能，包括MVTec AD上86.2% mAD、VisA上80.7% mAD和Real-IAD上73.1% mAD。诊断分析进一步显示异常泄漏减少、正常类别可分性提高以及异常-正常分数分离更强。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
