用条件隐空间增强异常检测，提升准确率与可解释性。
Improved Anomaly Detection through Conditional Latent Space VAE Ensembles
- 为每类数据定制先验分布，构建条件隐空间VAE
- MNIST上达97.4% AUC，优于传统VAE和CNN
- 小模型也能学复杂模式，适合工业异常检测
我们提出一种新型条件隐空间变分自编码器（CL-VAE），用于已知正常类、未知异常类的数据集上的异常检测预处理。该方法通过在数据中引入条件信息，增强隐空间分离能力，为每个类别拟合独特先验分布，将经典高斯先验扩展为高斯混合模型。多个此类VAE在隐空间集成形成共识，显著提升跨数据集的异常检测精度。实验对比了典型VAE、CNN和PCA的AUC表现，所提模型在MNIST数据集上达到97.4% AUC，优于第二佳模型的95.7%。此外，CL-VAE在集成中收益更大，隐空间更具可解释性，且在模型规模受限下仍能有效学习复杂数据模式。
原文摘要 · Abstract (English)
We propose a novel Conditional Latent space Variational Autoencoder (CL-VAE) to perform improved pre-processing for anomaly detection on data with known inlier classes and unknown outlier classes. This proposed variational autoencoder (VAE) improves latent space separation by conditioning on information within the data. The method fits a unique prior distribution to each class in the dataset, effectively expanding the classic prior distribution for VAEs to include a Gaussian mixture model. An ensemble of these VAEs are merged in the latent spaces to form a group consensus that greatly improves the accuracy of anomaly detection across data sets. Our approach is compared against the capabilities of a typical VAE, a CNN, and a PCA, with regards AUC for anomaly detection. The proposed model shows increased accuracy in anomaly detection, achieving an AUC of 97.4% on the MNIST dataset compared to 95.7% for the second best model. In addition, the CL-VAE shows increased benefits from ensembling, a more interpretable latent space, and an increased ability to learn patterns in complex data with limited model sizes.
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