PLAG boosts tabular anomaly detection by using pseudo-label-guided synthetic anomaly generation with a two-stage filter, achieving SOTA results and lifting F1 scores by 0.08-0.21 when added to existing detectors.
arXiv preprint arXiv:2403.19735 (2024)
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Fall detection and prediction are reformulated as anomaly detection tasks within an agentic AI system to enable adaptive, proactive risk management in human movement.
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Enhancing Tabular Anomaly Detection via Pseudo-Label-Guided Generation
PLAG boosts tabular anomaly detection by using pseudo-label-guided synthetic anomaly generation with a two-stage filter, achieving SOTA results and lifting F1 scores by 0.08-0.21 when added to existing detectors.
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Integrating Anomaly Detection into Agentic AI for Proactive Risk Management in Human Activity
Fall detection and prediction are reformulated as anomaly detection tasks within an agentic AI system to enable adaptive, proactive risk management in human movement.