Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Confidence-based Out-of-Distribution Detection: A Comparative Study and
  Analysis   Image classification models deployed in the real world may receive inputs
outside the intended data distribution. For critical applications such as
clinical decision making, it is important that a model can detect such
out-of-distribution (OOD) inputs and express its uncertainty. In this work, we
assess the capability of various state-of-the-art approaches for
confidence-based OOD detection through a comparative study and in-depth
analysis. First, we leverage a computer vision benchmark to reproduce and
compare multiple OOD detection methods. We then evaluate their capabilities on
the challenging task of disease classification using chest X-rays. Our study
shows that high performance in a computer vision task does not directly
translate to accuracy in a medical imaging task. We analyse factors that affect
performance of the methods between the two tasks. Our results provide useful
insights for developing the next generation of OOD detection methods.

21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin