That's BAD: Blind Anomaly Detection by Implicit Local Feature Clustering
Jie Zhang Masanori Suganuma Takayuki Okatani
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.