RTTAD improves unsupervised tabular anomaly detection by combining collaborative dual-task learning during training with selective, risk-aware test-time contrastive learning that avoids anomaly contamination.
Deep autoencoding gaussian mixture model for unsupervised anomaly detection
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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.
A problem-oriented taxonomy groups anomaly detection metrics into six dimensions and experiments show that some popular ones like NAB and Point-Adjust fail to resist random-score inflation.
citing papers explorer
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When Normality Shifts: Risk-Aware Test-Time Adaptation for Unsupervised Tabular Anomaly Detection
RTTAD improves unsupervised tabular anomaly detection by combining collaborative dual-task learning during training with selective, risk-aware test-time contrastive learning that avoids anomaly contamination.
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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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A Problem-Oriented Taxonomy of Evaluation Metrics for Time Series Anomaly Detection
A problem-oriented taxonomy groups anomaly detection metrics into six dimensions and experiments show that some popular ones like NAB and Point-Adjust fail to resist random-score inflation.