Recent studies on visual anomaly detection (AD) of industrial objects/textures have achieved quite good performance. They consider an unsupervised setting, specifically the one-class setting, in which we assume the availability of a set of normal (\textit{i.e.}, anomaly-free) images for training. In this paper, we consider a more challenging scenario of unsupervised AD, in which we detect anomalies in a given set of images that might contain both normal and anomalous samples. The setting does not assume the availability of known normal data and thus is completely free from human annotation, which differs from the standard AD considered in recent studies. For clarity, we call the setting blind anomaly detection (BAD). We show that BAD can be converted into a local outlier detection problem and propose a novel method named PatchCluster that can accurately detect image- and pixel-level anomalies. Experimental results show that PatchCluster shows a promising performance without the knowledge of normal data, even comparable to the SOTA methods applied in the one-class setting needing it.

翻译：近期关于工业物体/纹理的视觉异常检测研究已取得相当好的性能。这些研究考虑无监督设置，特别是单类设置，即假设可获取一组正常（即无异常）图像用于训练。本文考虑更具挑战性的无监督异常检测场景：在给定的图像集合中检测可能同时包含正常与异常样本的异常现象。该设置不假设已知正常数据的存在，因此完全免于人工标注，这与近期研究中考虑的标准异常检测不同。为明确区分，我们将此设置称为盲异常检测。我们证明盲异常检测可转化为局部离群点检测问题，并提出名为PatchCluster的新方法，该方法能准确检测图像级与像素级异常。实验结果表明，PatchCluster在无正常数据先验知识的情况下展现出有前景的性能，甚至可与需要正常数据的单类设置中最先进的SOTA方法相媲美。