541 citations · 564 across the 6 of their papers we have counts for
6 papers
IMPACT: Influence Modeling for Open-Set Time Series Anomaly Detection
Xiaohui Zhou, Yijie Wang, Hongzuo Xu +3
Open-set anomaly detection (OSAD) is an emerging paradigm designed to utilize limited labeled data from anomaly classes seen in training to identify both seen and unseen anomalies…
Self-Supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection
Yutong Chen, Hongzuo Xu, Guansong Pang +3
Time Series Anomaly Detection (TSAD) finds widespread applications across various domains such as financial markets, industrial production, and healthcare. Its primary objective is…
RoSAS: Deep Semi-Supervised Anomaly Detection with Contamination-Resilient Continuous Supervision
Hongzuo Xu, Yijie Wang, Guansong Pang +3
Semi-supervised anomaly detection methods leverage a few anomaly examples to yield drastically improved performance compared to unsupervised models. However, they still suffer from…
Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning
Hongzuo Xu, Yijie Wang, Juhui Wei +3
Due to the unsupervised nature of anomaly detection, the key to fueling deep models is finding supervisory signals. Different from current reconstruction-guided generative models a…
Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection
Hongzuo Xu, Yijie Wang, Songlei Jian +3
Time series anomaly detection is instrumental in maintaining system availability in various domains. Current work in this research line mainly focuses on learning data normality de…
Deep Isolation Forest for Anomaly Detection
Hongzuo Xu, Guansong Pang, Yijie Wang +1
Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector in recent years due to its general effectiveness across different benchmarks and strong s…