Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Against Adversarial Learning: Naturally Distinguish Known and Unknown in
  Open Set Domain Adaptation   Open set domain adaptation refers to the scenario that the target domain
contains categories that do not exist in the source domain. It is a more common
situation in the reality compared with the typical closed set domain adaptation
where the source domain and the target domain contain the same categories. The
main difficulty of open set domain adaptation is that we need to distinguish
which target data belongs to the unknown classes when machine learning models
only have concepts about what they know. In this paper, we propose an "against
adversarial learning" method that can distinguish unknown target data and known
data naturally without setting any additional hyper parameters and the target
data predicted to the known classes can be classified at the same time.
Experimental results show that the proposed method can make significant
improvement in performance compared with several state-of-the-art methods.

5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin