Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generative adversarial network-based image super-resolution using
  perceptual content losses   In this paper, we propose a deep generative adversarial network for
super-resolution considering the trade-off between perception and distortion.
Based on good performance of a recently developed model for super-resolution,
i.e., deep residual network using enhanced upscale modules (EUSR), the proposed
model is trained to improve perceptual performance with only slight increase of
distortion. For this purpose, together with the conventional content loss,
i.e., reconstruction loss such as L1 or L2, we consider additional losses in
the training phase, which are the discrete cosine transform coefficients loss
and differential content loss. These consider perceptual part in the content
loss, i.e., consideration of proper high frequency components is helpful for
the trade-off problem in super-resolution. The experimental results show that
our proposed model has good performance for both perception and distortion, and
is effective in perceptual super-resolution applications.

2
The categories of this paper are: math
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
16
The categories of this paper are: stat