Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Soft Labeling Affects Out-of-Distribution Detection of Deep Neural
  Networks   Soft labeling becomes a common output regularization for generalization and
model compression of deep neural networks. However, the effect of soft labeling
on out-of-distribution (OOD) detection, which is an important topic of machine
learning safety, is not explored. In this study, we show that soft labeling can
determine OOD detection performance. Specifically, how to regularize outputs of
incorrect classes by soft labeling can deteriorate or improve OOD detection.
Based on the empirical results, we postulate a future work for OOD-robust DNNs:
a proper output regularization by soft labeling can construct OOD-robust DNNs
without additional training of OOD samples or modifying the models, while
improving classification accuracy.

12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph