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.

翻译：基于重构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至通用场景仍面临挑战。单个模型需在避免复制异常细节的同时重构多样的正常外观，这暴露了两个耦合的失效模式：恒等捷径（异常通过重构路径）与误重构（正常类别间相互混淆）。我们提出\textbf{BoRAD}，一种无标签训练框架，将这一问题视为表示容量分配问题。BoRAD利用共享可学习原型库施加两类互补正则化项：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持原型间结构并提升对异常角度偏移的敏感性。原型库与预测头仅在训练阶段使用；推理过程保持标准师生特征差异传递，无需类别标签、负样本对、记忆检索或原型查询。BoRAD在多项通用异常检测任务中取得竞争性性能，包括MVTec AD上86.2% mAD、VisA上80.7% mAD及Real-IAD上73.1% mAD。诊断分析进一步显示其降低了异常泄露，提升了正常类别可分性及异常-正常分数分离度。