Abstract: Anomaly detection is a hot topic that has been widely studied in the field of machine learning and plays an important role in industrial production,food safety,disease monitoring,etc.The latest anomaly detection methods mostly jointly train semi-supervised detection models based on a small number of available labeled samples and many unlabeled samples.However,these existing semi-supervised anomaly detection models mostly use deep learning frameworks.Due to the lack of enough feature information on low-dimensional data sets,it is difficult to learn accurate data boundaries,resulting in insufficient detection perfor-mance.To solve this problem,a dually encoded semi-supervised anomaly detection(DE-SAD)model is proposed.DE-SAD can make full use of a small amount of available labeled data and a large amount of unlabeled data for semi-supervised learning,and learn more accurate implicit manifold distribution of normal data through the dually encoded stage constraint,thus effectively magnifying the gap between normal data and abnormal data.DE-SAD shows excellent ano-maly detection performance on multiple anomaly detection datasets from different fields,especially on low-dimensional data,and its AUROC is up to 4.6% higher than the current state-of-the-art methods.

Key words: Anomaly detection, Semi-supervised learning, Autoencoder, Low-dimensional data