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Multi-Modal Anomaly Detection: A Survey
Xudong Mou, Zexin Wu, Chuan Luo +4
Multi-Modal Anomaly Detection (MMAD) detects rare abnormal events from heterogeneous data sources and is increasingly used in safety- and reliability-critical applications such as…
An Improved Time Series Anomaly Detection by Applying Structural Similarity
Tiejun Wang, Rui Wang, Xudong Mou +4
Effective anomaly detection in time series is pivotal for modern industrial applications and financial systems. Due to the scarcity of anomaly labels and the high cost of manual la…
CAPMix: Robust KPI Anomaly Detection for AIOps in Noisy and Dynamic Environments
Xudong Mou, Rui Wang, Tiejun Wang +11
Time-series anomaly detection is crucial in AIOps for maintaining large-scale service reliability. In production, streams of Key Performance Indicators (KPI) are high-dimensional,…
RoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated Data
Xudong Mou, Rui Wang, Bo Li +4
The accumulation of time-series signals and the absence of labels make time-series Anomaly Detection (AD) a self-supervised task of deep learning. Methods based on normality assump…
Deep Contrastive One-Class Time Series Anomaly Detection
Rui Wang, Chongwei Liu, Xudong Mou +5
The accumulation of time-series data and the absence of labels make time-series Anomaly Detection (AD) a self-supervised deep learning task. Single-normality-assumption-based metho…