  When first deploying an anomaly detection system, e.g., to detect
out-of-scope queries in chatbots, there are no observed data, making
data-driven approaches ineffective. Zero-shot anomaly detection methods offer a
solution to such "cold-start" cases, but unfortunately they are often not
accurate enough. This paper studies the realistic but underexplored cold-start
setting where an anomaly detection model is initialized using zero-shot
guidance, but subsequently receives a small number of contaminated observations
(namely, that may include anomalies). The goal is to make efficient use of both
the zero-shot guidance and the observations. We propose ColdFusion, a method
that effectively adapts the zero-shot anomaly detector to contaminated
observations. To support future development of this new setting, we propose an
evaluation suite consisting of evaluation protocols and metrics.
