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
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Cold-Start Anomaly Detection | BANKING77-OOS | ColdFusion | AUC 10% | 81.7 | #1 of 3 | Archive leaderboard | report |
| Cold-Start Anomaly Detection | BANKING77-OOS | ZS | AUC 10% | 78.9 | #2 of 3 | Archive leaderboard | report |
| Cold-Start Anomaly Detection | BANKING77-OOS | DN2 | AUC 10% | 76.7 | #3 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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