Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Implicit Pairs for Boosting Unpaired Image-to-Image Translation   In image-to-image translation the goal is to learn a mapping from one image
domain to another. In the case of supervised approaches the mapping is learned
from paired samples. However, collecting large sets of image pairs is often
either prohibitively expensive or not possible. As a result, in recent years
more attention has been given to techniques that learn the mapping from
unpaired sets.
  In our work, we show that injecting implicit pairs into unpaired sets
strengthens the mapping between the two domains, improves the compatibility of
their distributions, and leads to performance boosting of unsupervised
techniques by over 14% across several measurements.
  The competence of the implicit pairs is further displayed with the use of
pseudo-pairs, i.e., paired samples which only approximate a real pair. We
demonstrate the effect of the approximated implicit samples on image-to-image
translation problems, where such pseudo-pairs may be synthesized in one
direction, but not in the other. We further show that pseudo-pairs are
significantly more effective as implicit pairs in an unpaired setting, than
directly using them explicitly in a paired setting.

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The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
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The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
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The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
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The categories of this paper are: funct-an