Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning multiple visual domains with residual adapters   There is a growing interest in learning data representations that work well
for many different types of problems and data. In this paper, we look in
particular at the task of learning a single visual representation that can be
successfully utilized in the analysis of very different types of images, from
dog breeds to stop signs and digits. Inspired by recent work on learning
networks that predict the parameters of another, we develop a tunable deep
network architecture that, by means of adapter residual modules, can be steered
on the fly to diverse visual domains. Our method achieves a high degree of
parameter sharing while maintaining or even improving the accuracy of
domain-specific representations. We also introduce the Visual Decathlon
Challenge, a benchmark that evaluates the ability of representations to capture
simultaneously ten very different visual domains and measures their ability to
recognize well uniformly.

12
The categories of this paper are: nucl-th
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph