Anomaly Detection Method Based on Siamese Attention Representation Network

Key words:  anomaly detection  Siamese network  multi-head attention  representation learning  contrastive self-supervised learning  weakly supervised learning

Abstract:

To solve the limitations of the unsupervised anomaly detection method,which cannot utilize the prior knowledge of anomalies, relies heavily on the anomaly distribution hypothesis, and is insensitive to edge anomalies near normal data,a contrastive learning method based on Siamese attention representation network is proposed to realize anomaly detection. In the method, two encoder networks constructed using multi-head attention learn different representations of the same unlabeled data, and a self-supervised representation learning model is implemented from one encoder network to another encoder network using contrast learning structure. Then, a classifier network is added to realize the detection of abnormal data through weakly supervised classification learning based on a small amount of labeled data. The experimental results show that this method can effectively improve the recall rate of anomaly detection and can be applied in the field of data analysis.