Visual anomaly detection targets to detect images that notably differ from normal pattern, and it has found extensive application in identifying defective parts within the manufacturing industry. These anomaly detection paradigms predominantly focus on training detection models using only clean, unlabeled normal samples, assuming an absence of contamination; a condition often unmet in real-world scenarios. The performance of these methods significantly depends on the quality of the data and usually decreases when exposed to noise. We introduce a systematic adaptive method that employs deviation learning to compute anomaly scores end-to-end while addressing data contamination by assigning relative importance to the weights of individual instances. In this approach, the anomaly scores for normal instances are designed to approximate scalar scores obtained from the known prior distribution. Meanwhile, anomaly scores for anomaly examples are adjusted to exhibit statistically significant deviations from these reference scores. Our approach incorporates a constrained optimization problem within the deviation learning framework to update instance weights, resolving this problem for each mini-batch. Comprehensive experiments on the MVTec and VisA benchmark datasets indicate that our proposed method surpasses competing techniques and exhibits both stability and robustness in the presence of data contamination.

翻译：视觉异常检测旨在检测与正常模式显著不同的图像，该技术已在制造业缺陷部件识别中得到广泛应用。现有异常检测范式主要集中于仅使用清洁、无标签的正常样本训练检测模型，其前提假设是数据不存在污染——这一条件在实际场景中往往无法满足。此类方法的性能严重依赖于数据质量，且在存在噪声时通常会显著下降。本文提出一种系统化的自适应方法，该方法采用偏差学习以端到端方式计算异常分数，同时通过为单个样本权重分配相对重要性来解决数据污染问题。在此方法中，正常样本的异常分数被设计为逼近从已知先验分布获得的标量分数，而异常样本的异常分数则被调整以呈现相对于这些参考分数的统计显著偏差。我们的方法在偏差学习框架内引入约束优化问题来更新样本权重，并在每个小批量数据中求解该优化问题。在MVTec和VisA基准数据集上的综合实验表明，所提方法在存在数据污染的情况下不仅超越了现有技术，同时展现出优异的稳定性与鲁棒性。