Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Open-Set Recognition Using Intra-Class Splitting   This paper proposes a method to use deep neural networks as end-to-end
open-set classifiers. It is based on intra-class data splitting. In open-set
recognition, only samples from a limited number of known classes are available
for training. During inference, an open-set classifier must reject samples from
unknown classes while correctly classifying samples from known classes. The
proposed method splits given data into typical and atypical normal subsets by
using a closed-set classifier. This enables to model the abnormal classes by
atypical normal samples. Accordingly, the open-set recognition problem is
reformulated into a traditional classification problem. In addition, a
closed-set regularization is proposed to guarantee a high closed-set
classification performance. Intensive experiments on five well-known image
datasets showed the effectiveness of the proposed method which outperformed the
baselines and achieved a distinct improvement over the state-of-the-art
methods.

30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg