Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adverse Weather Image Translation with Asymmetric and Uncertainty-aware
  GAN   Adverse weather image translation belongs to the unsupervised image-to-image
(I2I) translation task which aims to transfer adverse condition domain (eg,
rainy night) to standard domain (eg, day). It is a challenging task because
images from adverse domains have some artifacts and insufficient information.
Recently, many studies employing Generative Adversarial Networks (GANs) have
achieved notable success in I2I translation but there are still limitations in
applying them to adverse weather enhancement. Symmetric architecture based on
bidirectional cycle-consistency loss is adopted as a standard framework for
unsupervised domain transfer methods. However, it can lead to inferior
translation result if the two domains have imbalanced information. To address
this issue, we propose a novel GAN model, i.e., AU-GAN, which has an asymmetric
architecture for adverse domain translation. We insert a proposed feature
transfer network (${T}$-net) in only a normal domain generator (i.e., rainy
night-> day) to enhance encoded features of the adverse domain image. In
addition, we introduce asymmetric feature matching for disentanglement of
encoded features. Finally, we propose uncertainty-aware cycle-consistency loss
to address the regional uncertainty of a cyclic reconstructed image. We
demonstrate the effectiveness of our method by qualitative and quantitative
comparisons with state-of-the-art models. Codes are available at
https://github.com/jgkwak95/AU-GAN.

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The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
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The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-th
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The categories of this paper are: physics
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-fin
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The categories of this paper are: alg-geom
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The categories of this paper are: cmp-lg