An Anomaly Detection Method Combining Mutual Information Estimation with Adversarial Autoencoder

According to the information theory, the training objective of the unsupervised deep learning networks can be interpreted as maximizing the mutual information between the training samples and their representations. Adversarial autoencoder (AAE) learns the distribution of the training samples by the generative adversarial method. So the semi-supervised anomaly detection model based on the normal sample sets can be established using AAE. However, AAE cannot maximize the mutual information between the normal samples and their representations explicitly. A semi-supervised anomaly detection method based on mutual information estimation network and AAE (IAAE) is proposed. Firstly, the encoder and decoder of the AAE are trained to minimize the reconstruction error. Then, in the adversarial regularization stage, the aggregated posterior of the normal sample's representations are matched to the arbitrary prior distribution, and the mutual information between normal samples and their representations is maximized. Finally, the mutual information between normal samples and their representations are estimated by fully connected neural network. The reconstruction error of the test sample and its mode divergence in the hidden space are used to calculate the abnormal score. The experimental results on public datasets show that the IAAE has better performance than the existing typical deep anomaly detection models in terms of F1 values. © 2021, Editorial Department of Journal of Beijing University of Posts and Telecommunications. All right reserved.