  Several density estimation methods have shown to fail to detect
out-of-distribution (OOD) samples by assigning higher likelihoods to anomalous
data. Energy-based models (EBMs) are flexible, unnormalized density models
which seem to be able to improve upon this failure mode. In this work, we
provide an extensive study investigating OOD detection with EBMs trained with
different approaches on tabular and image data and find that EBMs do not
provide consistent advantages. We hypothesize that EBMs do not learn semantic
features despite their discriminative structure similar to Normalizing Flows.
To verify this hypotheses, we show that supervision and architectural
restrictions improve the OOD detection of EBMs independent of the training
approach.
