Abnormal behaviors in industrial systems may be early warnings on critical events that may cause severe damages to facilities and security. Thus, it is important to detect abnormal behaviors accurately and timely. However, the anomaly detection problem is hard to solve in practice, mainly due to the rareness and the expensive cost to get the labels of the anomalies. Deep generative models parameterized by neural networks have achieved state-of-the-art performance in practice for many unsupervised and semisupervised learning tasks. We present a new deep generative model, Latent Enhanced regression/classification Deep Generative Model (LEDGM), for the anomaly detection problem with multidimensional data. Instead of using two-stage decoupled models, we adopt an end-to-end learning paradigm. Instead of conditioning the latent on the class label, LEDGM conditions the label prediction on the learned latent so that the optimization goal is more in favor of better anomaly detection than better reconstruction that the previously proposed deep generative models have been trained for. Experimental results on several synthetic and real-world small- and large-scale datasets demonstrate that LEDGM can achieve improved anomaly detection performance on multidimensional data with very sparse labels. The results also suggest that both labeled anomalies and labeled normal are valuable for semisupervised learning. Generally, our results show that better performance can be achieved with more labeled data. The ablation experiments show that both the original input and the learned latent provide meaningful information for LEDGM to achieve high performance.

中文翻译：

---

用于高维异常检测的深度生成模型的半监督训练


工业系统中的异常行为可能是对可能对设施和安全造成严重损害的严重事件的早期预警。因此，准确、及时地发现异常行为非常重要。然而，异常检测问题在实践中很难解决，主要是由于异常标签的稀有性和昂贵的成本。由神经网络参数化的深度生成模型在许多无监督和半监督学习任务的实践中取得了最先进的性能。我们提出了一种新的深度生成模型，即潜在增强回归/分类深度生成模型（LEDGM），用于多维数据的异常检测问题。我们不使用两阶段解耦模型，而是采用端到端学习范例。 LEDGM 不是根据类标签来调节潜在变量，而是根据学习到的潜在变量来调节标签预测，以便优化目标更有利于更好的异常检测，而不是先前提出的深度生成模型训练的更好重建。在几个合成的和真实世界的小型和大型数据集上的实验结果表明，LEDGM 可以在具有非常稀疏标签的多维数据上实现改进的异常检测性能。结果还表明，标记的异常和标记的正常对于半监督学习都很有价值。一般来说，我们的结果表明，使用更多标记数据可以实现更好的性能。消融实验表明，原始输入和学习到的潜在输入都为 LEDGM 实现高性能提供了有意义的信息。