Industrial anomaly detection suffers from limited data, making cross-domain generalization particularly challenging. Generalist Anomaly Detection (GAD) aims to train a unified model on a source domain that can effectively detect anomalies in unseen target domains. In the initial semantic feature space, strong entanglement between anomalies and object categories or defect types hinders effective generalization across domains. Recent works address this issue by projecting features into a residual space; however, such methods primarily increase cross-domain overlap for normal features, while anomalous features remain specific to object categories, defect types and data domains, leading to poor alignment and generalization. To address this limitation, we propose Value-order Decomposition (VOD), a simple yet effective technique that bridges \textbf{three types of generalization gaps} across object categories, defect types (including real and synthetic defects), and data domains. VOD disentangles and suppresses object-category-, defect-type-, and domain-specific information, promoting alignment within normal and abnormal samples while preserving their separability, thereby enabling robust generalization across the three gaps. Leveraging the strong alignment between real and synthetic defects within the same object, we perform anomaly detection using only normal and synthetic-abnormal reference, and effectively generalize to unseen real defect types. Experiments on diverse industrial and medical benchmarks demonstrate that our method, using a simple cut-and-paste anomaly simulation strategy, achieves strong generalization across the three gaps.

翻译：工业异常检测面临数据稀缺的问题，这使得跨域泛化极具挑战性。通才型异常检测（GAD）旨在训练一个在源域上的统一模型，使其能够有效检测未见目标域中的异常。在初始语义特征空间中，异常与物体类别或缺陷类型之间的强纠缠阻碍了跨域有效泛化。近期工作通过将特征投影到残差空间来解决此问题；然而，这类方法主要增加了正常特征的跨域重叠，而异常特征仍与物体类别、缺陷类型和数据域紧密相关，导致对齐与泛化效果不佳。为解决这一局限，我们提出价值序分解（VOD），一种简单而有效的技术，它弥合了横跨物体类别、缺陷类型（包括真实与合成缺陷）以及数据域的**三种泛化鸿沟**。VOD 解耦并抑制了与物体类别、缺陷类型和数据域相关的特定信息，促进了正常与异常样本内部的对齐，同时保持两者间的可分性，从而实现对三大鸿沟的稳健泛化。利用同一物体内真实缺陷与合成缺陷之间的强对齐性，我们仅使用正常样本与合成异常参考进行异常检测，并有效泛化至未见过的真实缺陷类型。在多种工业与医学基准上的实验表明，采用简单的剪贴式异常模拟策略，我们的方法在三大鸿沟上均实现了强泛化。