Center-Aware Adversarial Autoencoder for Anomaly Detection.

Abstract

Anomaly detection based on subspace learning has attracted much attention, in which the compactness of subspace is commonly considered as the core concern. Most related studies directly optimize the distance from the subspace representation to the fixed center, and the influence of the anomaly level of each normal sample is not considered to adjust the normal concentrated areas. In such cases, it is difficult to isolate the normal areas from the anomaly ones by making the subspace compact. To this end, we propose a center-aware adversarial autoencoder (CA-AAE) method, which detects anomaly samples by acquiring more compact and discriminative subspace representations. To fully exploit the subspace information to improve the compactness, anomaly-level description and feature learning are novelly integrated herein by dividing the output space of the encoder into presubspace and postsubspace. In presubspace, the toward-center prior distribution is imposed by the adversarial learning mechanism, and the anomaly level of normal samples can be described from a probabilistic perspective. In postsubspace, a novel center-aware strategy is established to enhance the compactness of the postsubspace, which achieves adaptive adjustment of the normal areas by constructing a weighted center based on the anomaly level. Then, a flexible anomaly score function is constructed in the testing stage, in which both the toward-center loss and the reconstruction loss are combined to balance the information in the learned subspace and the original space. Compared to other related methods, the proposed CA-AAE shows the effectiveness and advantages in numerical experiments.