提出新异常检测场景与评估指标，解决真实世界中正常样本定义模糊问题。
Novel Anomaly Detection Scenarios and Evaluation Metrics to Address the Ambiguity in the Definition of Normal Samples
- 设计新检测场景，允许正常样本包含微小瑕疵，模拟工业升级需求。
- 引入RePaste机制，通过重贴高异常得分区域提升模型识别能力。
- 在MVTec AD上优于现有方法，适合工业质检等对标准变化敏感的应用。
传统异常检测仅使用正常样本训练，但在实际应用中，正常样本的定义常不明确。例如，某些带有轻微划痕或污渍的样本仍可接受，而设备升级后则需更高精度，此时微小划痕、尘粒或异物应被视作异常。此类情况在工业场景中频繁出现，但此前未受关注。为此，本文提出新型检测场景与评估指标，以应对实际应用中的规格变化。为缓解正常样本定义模糊问题，提出RePaste方法：将前一步高异常得分区域重新粘贴回输入，增强模型学习。在MVTec AD基准上的实验表明，该方法在新评估指标下达到当前最优性能，同时保持高AUROC和PRO分数。
原文摘要 · Abstract (English)
In conventional anomaly detection, training data consist of only normal samples. However, in real-world scenarios, the definition of a normal sample is often ambiguous. For example, there are cases where a sample has small scratches or stains but is still acceptable for practical usage. On the other hand, higher precision is required when manufacturing equipment is upgraded. In such cases, normal samples may include small scratches, tiny dust particles, or a foreign object that we would prefer to classify as an anomaly. Such cases frequently occur in industrial settings, yet they have not been discussed until now. Thus, we propose novel scenarios and an evaluation metric to accommodate specification changes in real-world applications. Furthermore, to address the ambiguity of normal samples, we propose the RePaste, which enhances learning by re-pasting regions with high anomaly scores from the previous step into the input for the next step. On our scenarios using the MVTec AD benchmark, RePaste achieved the state-of-the-art performance with respect to the proposed evaluation metric, while maintaining high AUROC and PRO scores. Code: https://github.com/ReijiSoftmaxSaito/Scenario
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