Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unpaired Image Super-Resolution with Optimal Transport Maps   Real-world image super-resolution (SR) tasks often do not have paired
datasets limiting the application of supervised techniques. As a result, the
tasks are usually approached by unpaired techniques based on Generative
Adversarial Networks (GANs) which yield complex training losses with several
regularization terms such as content and identity losses. We theoretically
investigate the optimization problems which arise in such models and find two
surprising observations. First, the learned SR map is always an optimal
transport (OT) map. Second, we empirically show that the learned map is biased,
i.e., it may not actually transform the distribution of low-resolution images
to high-resolution images. Inspired by these findings, we propose an algorithm
for unpaired SR which learns an unbiased OT map for the perceptual transport
cost. Unlike existing GAN-based alternatives, our algorithm has a simple
optimization objective reducing the neccesity to perform complex hyperparameter
selection and use additional regularizations. At the same time, it provides
nearly state-of-the-art performance on the large-scale unpaired AIM-19 dataset.

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The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-th
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The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: bayes-an
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The categories of this paper are: funct-an
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The categories of this paper are: supr-con
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The categories of this paper are: stat
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The categories of this paper are: dg-ga
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The categories of this paper are: math
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The categories of this paper are: nlin