Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Metric learning by Similarity Network for Deep Semi-Supervised Learning   Deep semi-supervised learning has been widely implemented in the real-world
due to the rapid development of deep learning. Recently, attention has shifted
to the approaches such as Mean-Teacher to penalize the inconsistency between
two perturbed input sets. Although these methods may achieve positive results,
they ignore the relationship information between data instances. To solve this
problem, we propose a novel method named Metric Learning by Similarity Network
(MLSN), which aims to learn a distance metric adaptively on different domains.
By co-training with the classification network, similarity network can learn
more information about pairwise relationships and performs better on some
empirical tasks than state-of-art methods.

19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex