6 papers
Synergizing Large Language Models and Task-specific Models for Time Series Anomaly Detection
Feiyi Chen, Leilei Zhang, Guansong Pang +2
In anomaly detection, methods based on large language models (LLMs) can incorporate expert knowledge by reading professional document, while task-specific small models excel at ext…
TimeRadar: A Domain-Rotatable Foundation Model for Time Series Anomaly Detection
Hui He, Hezhe Qiao, Yutong Chen +2
Current time series foundation models (TSFMs) primarily focus on learning prevalent and regular patterns within a predefined time or frequency domain to enable supervised downstrea…
AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection
Qihang Zhou, Guansong Pang, Yu Tian +2
Zero-shot anomaly detection (ZSAD) requires detection models trained using auxiliary data to detect anomalies without any training sample in a target dataset. It is a crucial task…
Open-Set Graph Anomaly Detection via Normal Structure Regularisation
Qizhou Wang, Guansong Pang, Mahsa Salehi +2
This paper considers an important Graph Anomaly Detection (GAD) task, namely open-set GAD, which aims to train a detection model using a small number of normal and anomaly nodes (r…
Deep Graph Anomaly Detection: A Survey and New Perspectives
Hezhe Qiao, Hanghang Tong, Bo An +3
Graph anomaly detection (GAD), which aims to identify unusual graph instances (nodes, edges, subgraphs, or graphs), has attracted increasing attention in recent years due to its si…
Generative Semi-supervised Graph Anomaly Detection
Hezhe Qiao, Qingsong Wen, Xiaoli Li +2
This work considers a practical semi-supervised graph anomaly detection (GAD) scenario, where part of the nodes in a graph are known to be normal, contrasting to the extensively ex…