Summary of Causaltad: Causal Implicit Generative Model For Debiased Online Trajectory Anomaly Detection, by Wenbin Li and Di Yao and Chang Gong and Xiaokai Chu and Quanliang Jing and Xiaolei Zhou and Yuxuan Zhang and Yunxia Fan and Jingping Bi
CausalTAD: Causal Implicit Generative Model for Debiased Online Trajectory Anomaly Detection
by Wenbin Li, Di Yao, Chang Gong, Xiaokai Chu, Quanliang Jing, Xiaolei Zhou, Yuxuan Zhang, Yunxia Fan, Jingping Bi
First submitted to arxiv on: 25 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to trajectory anomaly detection is proposed in this paper, which tackles the confounding bias caused by road network preference that existing methods ignore. The authors define a debiased trajectory anomaly detection problem and introduce CausalTAD, a causal implicit generative model that uses do-calculus to eliminate this bias. By estimating P(T|do(C)) as the anomaly criterion, CausalTAD achieves superior performance on both in-distribution and out-of-distribution trajectories, with improvements of 2.1% ~ 5.7% and 10.6% ~ 32.7%, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps to detect anomalies in trajectories, which is important for many real-world applications. Currently, existing methods calculate the probability P(T|C) as the anomaly risk, but they don’t take into account a common cause that affects both the trajectory and the source-destination pair. The new method, called CausalTAD, fixes this problem by removing the bias caused by road network preference. This leads to better results for both familiar and unfamiliar data. |
Keywords
» Artificial intelligence » Anomaly detection » Generative model » Probability
