论文标题
通过重组封闭式无监督专家的异常检测
Anomaly Detection by Recombining Gated Unsupervised Experts
论文作者
论文摘要
在先验知识的几个范围内考虑了异常检测。无监督的方法不需要任何标记的数据，而半监督的方法利用了一些已知的异常。受到专家的混合物模型的启发以及对神经网络的隐藏激活的分析，我们引入了一种新型数据驱动的异常检测方法，称为argue。我们的方法不仅适用于无监督和半监督的环境，还适用于自我监督设置的先验知识中的利润。我们设计的是专门的专家网络的组合，该网络专门研究一部分输入数据。为了最终决定，使用封闭式的专家体系结构进行融合在专家系统之间的分布式知识。我们的评估激发了有关正常数据分布的先验知识可能与已知异常一样有价值。
Anomaly detection has been considered under several extents of prior knowledge. Unsupervised methods do not require any labelled data, whereas semi-supervised methods leverage some known anomalies. Inspired by mixture-of-experts models and the analysis of the hidden activations of neural networks, we introduce a novel data-driven anomaly detection method called ARGUE. Our method is not only applicable to unsupervised and semi-supervised environments, but also profits from prior knowledge of self-supervised settings. We designed ARGUE as a combination of dedicated expert networks, which specialise on parts of the input data. For its final decision, ARGUE fuses the distributed knowledge across the expert systems using a gated mixture-of-experts architecture. Our evaluation motivates that prior knowledge about the normal data distribution may be as valuable as known anomalies.
