A Triplet Deviation Network Framework: Boosting Weakly-supervised Anomaly Detection By Ensemble Learning

Abstract

Weakly-supervised anomaly detection refers to extracting the information to identify anomalies from the limited labeled anomalies. Existing work has used the deviation network to learn the anomaly score end-to-end, which treats unlabeled data as normal data, and makes the output score of labeled anomalies deviate greatly from the normal data. However, due to the diversity of anomaly types, the model trained by limited labeled anomaly is one-sided, and can not be generalized to identify other types of anomalies. Simply treating unlabeled data as normal data can not extract features from unlabeled data to improve the generalization and accuracy of the model. In this paper, a triplet deviation network framework(TDNF) is proposed. Compared with the original deviation network, it adds a potential anomalies filtering module and a prior anomaly score generation module. The potential anomalies filtering module ensemble multiple unsupervised methods to evaluate data and filter potential anomalies. Labeled anomalies, potential anomalies, unlabeled data compose multiple triplets, and input to deviation network to improve the ability of the model to identify different types of anomalies. The prior anomaly score generation module uses one unsupervised method to generate normalized prior anomaly scores. The prior anomaly scores as prior knowledge of deviation network, which help to fine-tune the model's learning of unlabeled data to optimize the model's ability of anomaly ranking for unlabeled data. We give a triplet deviation network instance TDN-IHSC and carry out extensive experiments on multiple real-world datasets. The results show that our method is effective and performs better than the other four advanced competitive methods.