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.
Anomalygfm: Graph foun- dation model for zero/few-shot anomaly detection
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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.