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.

本文主要研究工业对象 / 纹理的视觉异常检测，提出了一种名为 PatchCluster 的新方法，能够准确检测图像和像素级别的异常情况，无需已知正常数据并且在性能上与需要已知正常数据的一类设置中的最先进方法相当。