From Zero to Hero: Cold-Start Anomaly Detection
<|reference_start|>From Zero to Hero: Cold-Start Anomaly Detection: 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.<|reference_end|>
@article{reiss2024from,
    title={From Zero to Hero: Cold-Start Anomaly Detection},
    author={Tal Reiss, George Kour, Naama Zwerdling, Ateret Anaby-Tavor, Yedid
  Hoshen},
    journal={arXiv preprint arXiv:2405.20341},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.20341},
    primaryClass={cs.LG cs.CL}
}