AI RESEARCH
When Normality Shifts: Risk-Aware Test-Time Adaptation for Unsupervised Tabular Anomaly Detection
arXiv CS.AI
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ArXi:2605.10242v1 Announce Type: cross Unsupervised tabular anomaly detection methods typically and subsequently identify samples that deviate from these patterns as anomalies during testing. However, in practical scenarios, the limited scale and diversity of