CenAD: Collaborative Embedding Network for Anomaly Detection with Leveraging Partially Observed Anomalies

Leveraging observed anomalies in anomaly detection can significantly improve detection accuracy. Assuming that observed anomalies cover all anomaly distributions, existing methods commonly learn the anomaly distributions from these observed anomalies and assign each object an anomaly score according to the similarities between it and observed anomalies. However, these observed anomalies may partially cover anomaly distributions, which severely restrains the performance in detecting uncovered anomalies. To address this issue, we propose a novel collaborative embedding network for this task, named CenAD. By leveraging partially observed anomalies, the collaborative learning derives a loss with maximum neighbor dispersion and minimum volume estimation as guidance to make anomalies more dispersed. Each object is assigned to an anomaly score by its contributions to data dispersion, which distinguishes these anomalies from the entire data effectively. To investigate the effectiveness of CenAD with partially observed anomalies, we conduct extensive results on several datasets to validate the superiority of our method, in which we obtain average improvement up to 13.92% in AUC-ROC and 29.44% in AUC-PR compared with previous methods.