Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Domain Adaptation in the Wild: Dealing with Asymmetric
  Label Sets   The goal of domain adaptation is to adapt models learned on a source domain
to a particular target domain. Most methods for unsupervised domain adaptation
proposed in the literature to date, assume that the set of classes present in
the target domain is identical to the set of classes present in the source
domain. This is a restrictive assumption that limits the practical
applicability of unsupervised domain adaptation techniques in real world
settings ("in the wild"). Therefore, we relax this constraint and propose a
technique that allows the set of target classes to be a subset of the source
classes. This way, large publicly available annotated datasets with a wide
variety of classes can be used as source, even if the actual set of classes in
target can be more limited and, maybe most importantly, unknown beforehand.
  To this end, we propose an algorithm that orders a set of source subspaces
that are relevant to the target classification problem. Our method then chooses
a restricted set from this ordered set of source subspaces. As an extension,
even starting from multiple source datasets with varied sets of categories,
this method automatically selects an appropriate subset of source categories
relevant to a target dataset. Empirical analysis on a number of source and
target domain datasets shows that restricting the source subspace to only a
subset of categories does indeed substantially improve the eventual target
classification accuracy over the baseline that considers all source classes.

23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
16
The categories of this paper are: stat
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con