  Today's deep models are often unable to detect inputs which do not belong to
the training distribution. This gives rise to confident incorrect predictions
which could lead to devastating consequences in many important application
fields such as healthcare and autonomous driving. Interestingly, both
discriminative and generative models appear to be equally affected.
Consequently, this vulnerability represents an important research challenge. We
consider an outlier detection approach based on discriminative training with
jointly learned synthetic outliers. We obtain the synthetic outliers by
sampling an RNVP model which is jointly trained to generate datapoints at the
border of the training distribution. We show that this approach can be adapted
for simultaneous semantic segmentation and dense outlier detection. We present
image classification experiments on CIFAR-10, as well as semantic segmentation
experiments on three existing datasets (StreetHazards, WD-Pascal, Fishyscapes
Lost & Found), and one contributed dataset. Our models perform competitively
with respect to the state of the art despite producing predictions with only
one forward pass.
