Weakly-supervised anomaly detection can outperform existing unsupervised methods with the assistance of a very small number of labeled anomalies, which attracts increasing attention from researchers. However, existing weakly-supervised anomaly detection methods are limited as these methods do not factor in the multimodel nature of the real-world data distribution. To mitigate this, we propose the Weakly-supervised Variational-mixture-model-based Anomaly Detector (WVAD). WVAD excels in multimodal datasets. It consists of two components: a deep variational mixture model, and an anomaly score estimator. The deep variational mixture model captures various features of the data from different clusters, then these features are delivered to the anomaly score estimator to assess the anomaly levels. Experimental results on three real-world datasets demonstrate WVAD's superiority.

翻译：弱监督异常检测方法在少量标记异常样本的辅助下能够超越现有无监督方法，因而日益受到研究者关注。然而，现有弱监督异常检测方法存在局限，未能充分考虑现实世界数据分布的多模态特性。为此，我们提出基于变分混合模型的弱监督异常检测器（WVAD）。WVAD在多模态数据集上表现优异，其包含两个核心组件：深度变分混合模型与异常评分估计器。深度变分混合模型从不同聚类中捕获数据的多样化特征，随后将这些特征传递至异常评分估计器以评估异常程度。在三个真实数据集上的实验结果验证了WVAD的优越性。