From Zero to Hero: Cold-Start Anomaly Detection
Tal Reiss, George Kour, Naama Zwerdling, Ateret Anaby Tavor, Yedid Hoshen · 2024
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.