  Despite most existing anomaly detection studies assume the availability of
normal training samples only, a few labeled anomaly examples are often
available in many real-world applications, such as defect samples identified
during random quality inspection, lesion images confirmed by radiologists in
daily medical screening, etc. These anomaly examples provide valuable knowledge
about the application-specific abnormality, enabling significantly improved
detection of similar anomalies in some recent models. However, those anomalies
seen during training often do not illustrate every possible class of anomaly,
rendering these models ineffective in generalizing to unseen anomaly classes.
This paper tackles open-set supervised anomaly detection, in which we learn
detection models using the anomaly examples with the objective to detect both
seen anomalies (`gray swans') and unseen anomalies (`black swans'). We propose
a novel approach that learns disentangled representations of abnormalities
illustrated by seen anomalies, pseudo anomalies, and latent residual anomalies
(i.e., samples that have unusual residuals compared to the normal data in a
latent space), with the last two abnormalities designed to detect unseen
anomalies. Extensive experiments on nine real-world anomaly detection datasets
show superior performance of our model in detecting seen and unseen anomalies
under diverse settings. Code and data are available at:
https://github.com/choubo/DRA.
