VESC: a new variational autoencoder based model for anomaly detection

Chunkai Zhang, Xinyu Wang, Jiahua Zhang, Shaocong Li, Hanyu Zhang, Chuanyi Liu, Peiyi Han

Abstract: Anomaly detection is a hot and practical problem. Most of the existing research is based on the model of the generative model, which judges abnormalities by comparing the data errors between original samples and reconstruction samples. Among them, Variational AutoEncoder (VAE) is widely used, but it has the problem of over-generalization. In this paper, we design an unsupervised deep learning anomaly detection method named VESC and propose the recursive reconstruction strategy. VESC adopts the idea of data compression and three structures on the basis of the original VAE, namely spatial constrained network, reformer structure, and re-encoder. The recursive reconstruction strategy can improve the accuracy of the model by increasing the number and typicality of training samples, and it can apply to most unsupervised learning methods. Experimental results of several benchmarks show that our model outperforms state-of-the-art anomaly detection methods. And our proposed strategy can improve the detection results of the original model.