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.

翻译：摘要：在多类别异常检测领域，源自单类别异常检测的基于重构的方法面临著名的“学习捷径”挑战，即模型未能按预期学习正常样本的模式，转而采用恒等映射或人工噪声消除等捷径策略。因此，模型无法将真实异常重构为正常实例，导致异常检测失效。为解决该问题，我们提出一种新型统一特征重构异常检测框架RLR（基于可学习参考表示的特征重构）。与先前方法不同，RLR利用可学习参考表示强制模型显式学习正常特征模式，从而避免模型陷入“学习捷径”问题。此外，RLR在可学习参考中引入局部约束以促进更有效的正常模式捕获，并采用掩码可学习键注意力机制增强鲁棒性。在包含15类对象的MVTec-AD数据集和12类对象的VisA数据集上的评估表明，RLR在统一设置下取得了优于现有最优方法的性能。RLR的代码将公开发布。