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Papers/From Zero to Hero: Cold-Start Anomaly Detection

From Zero to Hero: Cold-Start Anomaly Detection

Tal Reiss, George Kour, Naama Zwerdling, Ateret Anaby-Tavor, Yedid Hoshen

2024-05-30zero-shot anomaly detectionCold-Start Anomaly DetectionAnomaly Detection
PaperPDFCode(official)

Abstract

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.

Results

TaskDatasetMetricValueModel
Anomaly DetectionBANKING77-OOSAUC 10%81.7ColdFusion
Anomaly DetectionBANKING77-OOSAUC 10%78.9ZS
Anomaly DetectionBANKING77-OOSAUC 10%76.7DN2

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