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.
Mcm: Masked cell modeling for anomaly detection in tabular data
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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.
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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.