Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adaptive SVM+: Learning with Privileged Information for Domain
  Adaptation   Incorporating additional knowledge in the learning process can be beneficial
for several computer vision and machine learning tasks. Whether privileged
information originates from a source domain that is adapted to a target domain,
or as additional features available at training time only, using such
privileged (i.e., auxiliary) information is of high importance as it improves
the recognition performance and generalization. However, both primary and
privileged information are rarely derived from the same distribution, which
poses an additional challenge to the recognition task. To address these
challenges, we present a novel learning paradigm that leverages privileged
information in a domain adaptation setup to perform visual recognition tasks.
The proposed framework, named Adaptive SVM+, combines the advantages of both
the learning using privileged information (LUPI) paradigm and the domain
adaptation framework, which are naturally embedded in the objective function of
a regular SVM. We demonstrate the effectiveness of our approach on the publicly
available Animals with Attributes and INTERACT datasets and report
state-of-the-art results in both of them.

19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th