无需训练，用记忆检索实现顶尖异常检测效果
Is Task-Specific Training Necessary for Anomaly Detection?
- 不依赖特定任务训练，通过多层级检索匹配测试块与内存中的正常特征
- 在四个基准上达领先性能，单张正常图即可达96.7%像素AUROC
- 适合追求高效、少样本或部署受限场景的异常检测应用
当前最先进的多类无监督异常检测（MUAD）方法依赖编码器-解码器模型重建无异常特征，但此类任务特定训练在分布偏移下成本高昂，且基于重构的残差评分面临保真度与稳定性困境。现有免训练方法仍易出现跨类别和跨区域匹配错误。为此，我们提出基于检索的异常检测（RAD），将正常特征存入内存，通过多层次检索实现异常检测。实验表明，RAD在四个标准基准（MVTec-AD、VisA、Real-IAD、3D-ADAM）上均达领先性能，尤其在少样本设置下表现优异：仅需一张正常图像即在MVTec-AD上达到96.7%像素AUROC，接近全数据训练下98.5%的性能。结果挑战了MUAD必须进行任务特定训练的假设，证明顶级异常检测可仅通过免训练的内存检索实现。
原文摘要 · Abstract (English)
Current state-of-the-art multi-class unsupervised anomaly detection (MUAD) methods rely on training encoder--decoder models to reconstruct anomaly-free features. However, we argue that such task-specific training is costly under distribution shifts, and that reconstruction-based residual scoring further faces a fidelity--stability dilemma. Existing training-free alternatives, in turn, remain prone to cross-category and cross-region mismatches in MUAD. Motivated by these limitations, we propose Retrieval-based Anomaly Detection (RAD), a task-specific training-free framework that stores anomaly-free features in a memory and detects anomalies through multi-level retrieval, matching test patches against the memory. Experiments demonstrate that RAD achieves state-of-the-art performance across four established benchmarks (MVTec-AD, VisA, Real-IAD, 3D-ADAM) under both standard and few-shot settings. On MVTec-AD, RAD reaches 96.7% Pixel AUROC with just a single anomaly-free image compared to 98.5% of RAD's full-data performance. Collectively, these findings overturn the assumption that MUAD requires task-specific training, showing that state-of-the-art anomaly detection is feasible with training-free memory-based retrieval. Our code is available at https://github.com/longkukuhi/RAD.
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