Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Open-Set Crowdsourcing using Multiple-Source Transfer Learning   We raise and define a new crowdsourcing scenario, open set crowdsourcing,
where we only know the general theme of an unfamiliar crowdsourcing project,
and we don't know its label space, that is, the set of possible labels. This is
still a task annotating problem, but the unfamiliarity with the tasks and the
label space hampers the modelling of the task and of workers, and also the
truth inference. We propose an intuitive solution, OSCrowd. First, OSCrowd
integrates crowd theme related datasets into a large source domain to
facilitate partial transfer learning to approximate the label space inference
of these tasks. Next, it assigns weights to each source domain based on
category correlation. After this, it uses multiple-source open set transfer
learning to model crowd tasks and assign possible annotations. The label space
and annotations given by transfer learning will be used to guide and
standardize crowd workers' annotations. We validate OSCrowd in an online
scenario, and prove that OSCrowd solves the open set crowdsourcing problem,
works better than related crowdsourcing solutions.

37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph