  Deep neural networks are known to achieve superior results in classification
tasks. However, it has been recently shown that they are incapable to detect
examples that are generated by a distribution which is different than the one
they have been trained on since they are making overconfident prediction for
Out-Of-Distribution (OOD) examples. OOD detection has attracted a lot of
attention recently. In this paper, we review some of the most seminal recent
algorithms in the OOD detection field, we divide those methods into training
and post-training and we experimentally show how the combination of the former
with the latter can achieve state-of-the-art results in the OOD detection task.
