Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cycle-consistent Generative Adversarial Networks for Neural Style
  Transfer using data from Chang'E-4   Generative Adversarial Networks (GANs) have had tremendous applications in
Computer Vision. Yet, in the context of space science and planetary exploration
the door is open for major advances. We introduce tools to handle planetary
data from the mission Chang'E-4 and present a framework for Neural Style
Transfer using Cycle-consistency from rendered images. The experiments are
conducted in the context of the Iris Lunar Rover, a nano-rover that will be
deployed in lunar terrain in 2021 as the flagship of Carnegie Mellon, being the
first unmanned rover of America to be on the Moon.

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The categories of this paper are: atom-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-th
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The categories of this paper are: stat
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The categories of this paper are: mtrl-th
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The categories of this paper are: q-bio
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The categories of this paper are: alg-geom
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The categories of this paper are: math
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The categories of this paper are: q-fin
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The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
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The categories of this paper are: dg-ga