Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learn from Unpaired Data for Image Restoration: A Variational Bayes
  Approach   Collecting paired training data is difficult in practice, but the unpaired
samples broadly exist. Current approaches aim at generating synthesized
training data from the unpaired samples by exploring the relationship between
the corrupted and clean data. This work proposes LUD-VAE, a deep generative
method to learn the joint probability density function from data sampled from
marginal distributions. Our approach is based on a carefully designed
probabilistic graphical model in which the clean and corrupted data domains are
conditionally independent. Using variational inference, we maximize the
evidence lower bound (ELBO) to estimate the joint probability density function.
Furthermore, we show that the ELBO is computable without paired samples under
the inference invariant assumption. This property provides the mathematical
rationale of our approach in the unpaired setting. Finally, we apply our method
to real-world image denoising and super-resolution tasks and train the models
using the synthetic data generated by the LUD-VAE. Experimental results
validate the advantages of our method over other learnable approaches.

20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat