Although mainstream unsupervised anomaly detection (AD) algorithms perform well in academic datasets, their performance is limited in practical application due to the ideal experimental setting of clean training data. Training with noisy data is an inevitable problem in real-world anomaly detection but is seldom discussed. This paper considers label-level noise in image sensory anomaly detection for the first time. To solve this problem, we proposed a memory-based unsupervised AD method, SoftPatch, which efficiently denoises the data at the patch level. Noise discriminators are utilized to generate outlier scores for patch-level noise elimination before coreset construction. The scores are then stored in the memory bank to soften the anomaly detection boundary. Compared with existing methods, SoftPatch maintains a strong modeling ability of normal data and alleviates the overconfidence problem in coreset. Comprehensive experiments in various noise scenes demonstrate that SoftPatch outperforms the state-of-the-art AD methods on the MVTecAD and BTAD benchmarks and is comparable to those methods under the setting without noise.

翻译：尽管主流的无监督异常检测（AD）算法在学术数据集中表现优异，但由于理想化实验设置中训练数据的纯净性，其在实际应用中的性能受到限制。在现实场景的异常检测中，使用含噪声数据进行训练是难以回避的问题，然而这一议题鲜少被探讨。本文首次针对图像感知异常检测中的标签级噪声问题展开研究。为解决该问题，我们提出了一种基于记忆库的无监督异常检测方法SoftPatch，该方法能在图像块级别高效地去除噪声。通过噪声判别器生成离群值分数，在构建核心集前实现图像块级噪声消除。这些分数随后被存入记忆库，以软化异常检测边界。与现有方法相比，SoftPatch在保持对正常数据强建模能力的同时，缓解了核心集存在的过度自信问题。在多种噪声场景下的综合实验表明，SoftPatch在MVTecAD和BTAD基准测试中优于当前最先进的异常检测方法，且在无噪声设定下其性能与这些方法相当。