Prediction Based Deep Autoencoding Model for Anomaly Detection

Latent variables and reconstruction error generated from auto encoder are the common means for anomaly detection dealing with high dimensional signals. They are exclusively typical representations of the original input, and a plenty of methods utilizing them for anomaly detection have achieved good results. In this paper, we propose a new method combining these two features together to generate proper scores for anomaly detection. As both these two features contain useful information contributing to anomaly detection, good results can be expected by fusion of those two. The architecture proposed in this paper comprises of two networks, and we only use normal data for training. To compress and rebuild an input, a deep auto encoder (AE) is utilized where low dimensional latent variables and reconstruction error can be obtained, and compactness loss is introduced on latent variables to maintain a low intra-variance. Meanwhile, multi-layer perceptron (MLP) network which takes the generated latent variables as input is established aiming at predicting its corresponding reconstruction error. By introducing MLP network, anomalies sharing similar reconstruction error yet different distribution of latent variables to normal data or vice versa can be separated. These two networks, AE and MLP are trained jointly in our model and the prediction error form MLP network is used as the final score for anomaly detection. Experiments on several benchmarks including image and multivariable datasets demonstrate the effectiveness and practicability of this new approach when comparing with several up-to-data algorithms.