Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: AdaGraph: Unifying Predictive and Continuous Domain Adaptation through
  Graphs   The ability to categorize is a cornerstone of visual intelligence, and a key
functionality for artificial, autonomous visual machines. This problem will
never be solved without algorithms able to adapt and generalize across visual
domains. Within the context of domain adaptation and generalization, this paper
focuses on the predictive domain adaptation scenario, namely the case where no
target data are available and the system has to learn to generalize from
annotated source images plus unlabeled samples with associated metadata from
auxiliary domains. Our contributionis the first deep architecture that tackles
predictive domainadaptation, able to leverage over the information broughtby
the auxiliary domains through a graph. Moreover, we present a simple yet
effective strategy that allows us to take advantage of the incoming target data
at test time, in a continuous domain adaptation scenario. Experiments on three
benchmark databases support the value of our approach.

6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
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The categories of this paper are: math
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an