  Standard machine learning is unable to accommodate inputs which do not belong
to the training distribution. The resulting models often give rise to confident
incorrect predictions which may lead to devastating consequences. This problem
is especially demanding in the context of dense prediction since input images
may be only partially anomalous. Previous work has addressed dense
out-of-distribution detection by discriminative training with respect to
off-the-shelf negative datasets. However, real negative data are unlikely to
cover all modes of the entire visual world. To this end, we extend this
approach by generating synthetic negative patches along the border of the
inlier manifold. We leverage a jointly trained normalizing flow due to
coverage-oriented learning objective and the capability to generate samples at
different resolutions. We detect anomalies according to a principled
information-theoretic criterion which can be consistently applied through
training and inference. The resulting models set the new state of the art on
benchmarks for out-of-distribution detection in road-driving scenes and remote
sensing imagery, in spite of minimal computational overhead.
