Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Open-set Recognition via Augmentation-based Similarity Learning   The primary assumption of conventional supervised learning or classification
is that the test samples are drawn from the same distribution as the training
samples, which is called closed set learning or classification. In many
practical scenarios, this is not the case because there are unknowns or unseen
class samples in the test data, which is called the open set scenario, and the
unknowns need to be detected. This problem is referred to as the open set
recognition problem and is important in safety-critical applications. We
propose to detect unknowns (or unseen class samples) through learning pairwise
similarities. The proposed method works in two steps. It first learns a closed
set classifier using the seen classes that have appeared in training and then
learns how to compare seen classes with pseudo-unseen (automatically generated
unseen class samples). The pseudo-unseen generation is carried out by
performing distribution shifting augmentations on the seen or training samples.
We call our method OPG (Open set recognition based on Pseudo unseen data
Generation). The experimental evaluation shows that the learned
similarity-based features can successfully distinguish seen from unseen in
benchmark datasets for open set recognition.

11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph