  After an autoencoder (AE) has learnt to reconstruct one dataset, it might be
expected that the likelihood on an out-of-distribution (OOD) input would be
low. This has been studied as an approach to detect OOD inputs. Recent work
showed this intuitive approach can fail for the dataset pairs FashionMNIST vs
MNIST. This paper suggests this is due to the use of Bernoulli likelihood and
analyses why this is the case, proposing two fixes: 1) Compute the uncertainty
of likelihood estimate by using a Bayesian version of the AE. 2) Use
alternative distributions to model the likelihood.
