Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Selective Pseudo-Labeling with Reinforcement Learning for
  Semi-Supervised Domain Adaptation   Recent domain adaptation methods have demonstrated impressive improvement on
unsupervised domain adaptation problems. However, in the semi-supervised domain
adaptation (SSDA) setting where the target domain has a few labeled instances
available, these methods can fail to improve performance. Inspired by the
effectiveness of pseudo-labels in domain adaptation, we propose a reinforcement
learning based selective pseudo-labeling method for semi-supervised domain
adaptation. It is difficult for conventional pseudo-labeling methods to balance
the correctness and representativeness of pseudo-labeled data. To address this
limitation, we develop a deep Q-learning model to select both accurate and
representative pseudo-labeled instances. Moreover, motivated by large margin
loss's capacity on learning discriminative features with little data, we
further propose a novel target margin loss for our base model training to
improve its discriminability. Our proposed method is evaluated on several
benchmark datasets for SSDA, and demonstrates superior performance to all the
comparison methods.

31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph