Why Does Counterintuitive Likelihood Behavior Rarely Emerge in Tabular Anomaly Detection?
arXiv:2602.09593v3 Announce Type: replace-cross Abstract: Deep generative models with tractable likelihoods, exemplified by normalizing flows, offer an effective basis for anomaly detection through likelihood-based scoring. We demonstrate that, unlike in the image domain where deep generative models frequently assign higher likelihoods to anomalous data, such counterintuitive behavior occurs rarely in tabular settings. We first introduce a domain-agnostic formulation that enables consistent det…