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.

基于弱监督的变分混合模型的异常检测器（WVAD）在多模态数据集上表现出卓越性能。通过捕捉不同聚类中数据的各种特征，并通过异常得分评估器对这些特征进行评估，WVAD 能够识别异常水平。在三个真实世界数据集上的实验结果证明了 WVAD 的优越性。

多模态数据分布的弱监督异常检测