Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning image-to-image translation using paired and unpaired training
  samples   Image-to-image translation is a general name for a task where an image from
one domain is converted to a corresponding image in another domain, given
sufficient training data. Traditionally different approaches have been proposed
depending on whether aligned image pairs or two sets of (unaligned) examples
from both domains are available for training. While paired training samples
might be difficult to obtain, the unpaired setup leads to a highly
under-constrained problem and inferior results. In this paper, we propose a new
general purpose image-to-image translation model that is able to utilize both
paired and unpaired training data simultaneously. We compare our method with
two strong baselines and obtain both qualitatively and quantitatively improved
results. Our model outperforms the baselines also in the case of purely paired
and unpaired training data. To our knowledge, this is the first work to
consider such hybrid setup in image-to-image translation.

9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
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The categories of this paper are: bayes-an
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The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol