Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Why Should we Combine Training and Post-Training Methods for
  Out-of-Distribution Detection?   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.

20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int