Meta Pseudo Labels for Anomaly Detection via Partially Observed Anomalies

Sinong Zhao, Zhaoyang Yu, Xiaofei Wang, Trent G. Marbach, Gang Wang, Xiaoguang Liu

Abstract: General anomaly detection based on weakly supervised or partially observed anomalies has been an important research. However, most such algorithms treat the unlabeled set as a substitute for normal samples and ignore the potential anomalies in it, which fails make full use of the abnormal supervision information. To address this issue, we propose a meta-pseudo-label based framework for anomaly detection (MPAD). The framework strives to obtain effective pseudo anomalies from the unlabeled samples to supplement the observed anomaly set. Specifically, a teacher network is improved based on the feedback of a student network on a validation set, thereby generating more conducive pseudo anomalies to assist the student network while incurring less confirmation bias. Extensive experiments show that the proposed MPAD algorithm outperforms current popular algorithms on five real datasets.