Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: One-Class Feature Learning Using Intra-Class Splitting   This paper proposes a novel generic one-class feature learning method based
on intra-class splitting. In one-class classification, feature learning is
challenging, because only samples of one class are available during training.
Hence, state-of-the-art methods require reference multi-class datasets to
pretrain feature extractors. In contrast, the proposed method realizes feature
learning by splitting the given normal class into typical and atypical normal
samples. By introducing closeness loss and dispersion loss, an intra-class
joint training procedure between the two subsets after splitting enables the
extraction of valuable features for one-class classification. Various
experiments on three well-known image classification datasets demonstrate the
effectiveness of our method which outperformed other baseline models in
average.

30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas