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Despite most existing anomaly detection studies assume the availability ofnormal training samples only, a few labeled anomaly examples are oftenavailable in many real-world applications, such as defect samples identifiedduring random quality inspection, lesion images confirmed by radiologists indaily medical screening, etc. These anomaly examples provide valuable knowledgeabout the application-specific abnormality, enabling significantly improveddetection of similar anomalies in some recent models. However, those anomaliesseen 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 learndetection models using the anomaly examples with the objective to detect bothseen anomalies (`gray swans') and unseen anomalies (`black swans'). We proposea novel approach that learns disentangled representations of abnormalitiesillustrated by seen anomalies, pseudo anomalies, and latent residual anomalies(i.e., samples that have unusual residuals compared to the normal data in alatent space), with the last two abnormalities designed to detect unseenanomalies. Extensive experiments on nine real-world anomaly detection datasetsshow superior performance of our model in detecting seen and unseen anomaliesunder diverse settings. Code and data are available at:https://github.com/choubo/DRA.

Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection