Adapting prior-data fitted networks for tabular anomaly detection

Adapting prior-data fitted networks (PFNs) for tabular anomaly detection involves leveraging frozen TabPFN features to score samples via nearest-neighbor distances. This method addresses the challenge of detecting anomalies without supervised training or contaminated reference sets, providing a way to utilize deep representations in unsupervised tabular workflows.