Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semi-Supervised Multitask Learning on Multispectral Satellite Images
  Using Wasserstein Generative Adversarial Networks (GANs) for Predicting
  Poverty   Obtaining reliable data describing local poverty metrics at a granularity
that is informative to policy-makers requires expensive and logistically
difficult surveys, particularly in the developing world. Not surprisingly, the
poverty stricken regions are also the ones which have a high probability of
being a war zone, have poor infrastructure and sometimes have governments that
do not cooperate with internationally funded development efforts. We train a
CNN on free and publicly available daytime satellite images of the African
continent from Landsat 7 to build a model for predicting local economic
livelihoods. Only 5% of the satellite images can be associated with labels
(which are obtained from DHS Surveys) and thus a semi-supervised approach using
a GAN (similar to the approach of Salimans, et al. (2016)), albeit with a more
stable-to-train flavor of GANs called the Wasserstein GAN regularized with
gradient penalty(Gulrajani, et al. (2017)) is used. The method of multitask
learning is employed to regularize the network and also create an end-to-end
model for the prediction of multiple poverty metrics.

26
The categories of this paper are: bayes-an
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn