Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Teacher-Student Competition for Unsupervised Domain Adaptation   With the supervision from source domain only in class-level, existing
unsupervised domain adaptation (UDA) methods mainly learn the domain-invariant
representations from a shared feature extractor, which causes the source-bias
problem. This paper proposes an unsupervised domain adaptation approach with
Teacher-Student Competition (TSC). In particular, a student network is
introduced to learn the target-specific feature space, and we design a novel
competition mechanism to select more credible pseudo-labels for the training of
student network. We introduce a teacher network with the structure of existing
conventional UDA method, and both teacher and student networks compete to
provide target pseudo-labels to constrain every target sample's training in
student network. Extensive experiments demonstrate that our proposed TSC
framework significantly outperforms the state-of-the-art domain adaptation
methods on Office-31 and ImageCLEF-DA benchmarks.

18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph