提出一种无需标签的异常检测模型自动验证方法，提升无监督场景下的模型可靠性。
Towards Unsupervised Validation of Anomaly-Detection Models
- 借鉴现实协作决策机制，设计无监督模型验证新范式。
- 在模型选择与评估任务中均展现高准确性和鲁棒性。
- 适用于缺乏先验知识的自动化异常检测系统部署。
无监督异常检测模型的验证是一项极具挑战性的任务。尽管常规模型验证依赖于带标签的验证集，但在底层数据无标签时无法构建此类验证集。缺乏稳健且高效的无监督模型验证技术，严重制约了自动化异常检测流水线的应用，尤其是在对模型在类似数据集上的表现缺乏先验知识的情况下。本文提出一种受真实世界协作决策机制启发的新范式，用于自动化异常检测模型的验证。重点关注两种常见的无监督验证任务——模型选择与模型评估，并通过大量实验验证了该方法在两项任务中的准确性和鲁棒性。
原文摘要 · Abstract (English)
Unsupervised validation of anomaly-detection models is a highly challenging task. While the common practices for model validation involve a labeled validation set, such validation sets cannot be constructed when the underlying datasets are unlabeled. The lack of robust and efficient unsupervised model-validation techniques presents an acute challenge in the implementation of automated anomaly-detection pipelines, especially when there exists no prior knowledge of the model's performance on similar datasets. This work presents a new paradigm to automated validation of anomaly-detection models, inspired by real-world, collaborative decision-making mechanisms. We focus on two commonly-used, unsupervised model-validation tasks -- model selection and model evaluation -- and provide extensive experimental results that demonstrate the accuracy and robustness of our approach on both tasks.
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