Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Sample Selection Approach for Universal Domain Adaptation   We study the problem of unsupervised domain adaption in the universal
scenario, in which only some of the classes are shared between the source and
target domains. We present a scoring scheme that is effective in identifying
the samples of the shared classes. The score is used to select which samples in
the target domain to pseudo-label during training. Another loss term encourages
diversity of labels within each batch. Taken together, our method is shown to
outperform, by a sizable margin, the current state of the art on the literature
benchmarks.

36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg