  Deep probabilistic generative models enable modeling the likelihoods of very
high dimensional data. An important application of generative modeling should
be the ability to detect out-of-distribution (OOD) samples by setting a
threshold on the likelihood. However, some recent studies show that
probabilistic generative models can, in some cases, assign higher likelihoods
on certain types of OOD samples, making the OOD detection rules based on
likelihood threshold problematic. To address this issue, several OOD detection
methods have been proposed for deep generative models. In this paper, we make
the observation that many of these methods fail when applied to generative
models based on Variational Auto-encoders (VAE). As an alternative, we propose
Likelihood Regret, an efficient OOD score for VAEs. We benchmark our proposed
method over existing approaches, and empirical results suggest that our method
obtains the best overall OOD detection performances when applied to VAEs.
