In the field of multi-class anomaly detection, reconstruction-based methods derived from single-class anomaly detection face the well-known challenge of ``learning shortcuts'', wherein the model fails to learn the patterns of normal samples as it should, opting instead for shortcuts such as identity mapping or artificial noise elimination. Consequently, the model becomes unable to reconstruct genuine anomalies as normal instances, resulting in a failure of anomaly detection. To counter this issue, we present a novel unified feature reconstruction-based anomaly detection framework termed RLR (Reconstruct features from a Learnable Reference representation). Unlike previous methods, RLR utilizes learnable reference representations to compel the model to learn normal feature patterns explicitly, thereby prevents the model from succumbing to the ``learning shortcuts'' issue. Additionally, RLR incorporates locality constraints into the learnable reference to facilitate more effective normal pattern capture and utilizes a masked learnable key attention mechanism to enhance robustness. Evaluation of RLR on the 15-category MVTec-AD dataset and the 12-category VisA dataset shows superior performance compared to state-of-the-art methods under the unified setting. The code of RLR will be publicly available.

提出了一种名为RLR（从可学习的参考表示中重建特征的异常检测框架）的新型统一特征重建型异常检测框架，利用可学习的参考表示明确地使模型学习正常特征模式，从而防止模型受到'学习捷径'问题的影响，并融入了局部约束和掩码可学习键注意机制以增强鲁棒性。在15个类别的MVTec-AD数据集和12个类别的VisA数据集上评估RLR，在统一设置下表现出比现有方法更优秀的性能。RLR的代码将公开发布。

学习统一的参考表示用于无监督多类异常检测