Inspired by mixture-of-experts models and the analysis of the hidden activations of neural networks, we introduce a novel unsupervised anomaly detection method called ARGUE. Multiple expert networks, which specialise on parts of the data deemed as normal, contribute to the overall anomaly score. For its final decision, ARGUE weights the distributed knowledge across the expert systems using a gated mixture-of-experts architecture. ARGUE achieves superior detection performance across several domains in a purely data-driven fashion and is more robust to noisy data sets than other state-of-the-art anomaly detection methods.

翻译：在专家混合模型和对神经网络隐蔽引爆器分析的启发下,我们采用了一种新型的未经监督的异常探测方法,称为ARGUE。多专家网络专门研究被视为正常数据的某些部分,有助于总体异常分数。最后决定是,ARGUE使用封闭的专家混合结构对专家系统分布的知识进行加权。ARGUE以纯数据驱动的方式在多个领域取得优异的检测性能,而且比其他最先进的异常探测方法更能对吵闹的数据集进行更强的检测性能。