Summary of From Zero to Hero: Cold-start Anomaly Detection, by Tal Reiss et al.
From Zero to Hero: Cold-Start Anomaly Detection
by Tal Reiss, George Kour, Naama Zwerdling, Ateret Anaby-Tavor, Yedid Hoshen
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper focuses on the “cold-start” problem in anomaly detection, where no observed data is available for model initialization. Zero-shot methods are ineffective due to low accuracy. To address this issue, the authors propose ColdFusion, a method that adapts a zero-shot anomaly detector to contaminated observations using a small number of labeled examples. The approach efficiently utilizes both zero-shot guidance and observations to improve detection accuracy. The evaluation suite proposed in this paper provides benchmarks and metrics for future development in this underexplored setting. By addressing the limitations of current methods, ColdFusion aims to enhance the effectiveness of anomaly detection systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper solves a big problem in finding unusual patterns (anomalies) when we don’t have any data to start with. Currently, there are no good solutions for this “cold-start” case because existing methods aren’t accurate enough. The authors create a new way called ColdFusion that uses small amounts of labeled examples to improve anomaly detection accuracy. This approach makes the most of both zero-shot guidance and observations. The paper also proposes a set of evaluation metrics and protocols to help develop solutions for this challenging problem. |
Keywords
* Artificial intelligence * Anomaly detection * Zero shot
