Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Deep Neural Network for Photo-realistic Image Super-Resolution   Recent progress in deep learning-based models has improved photo-realistic
(or perceptual) single-image super-resolution significantly. However, despite
their powerful performance, many methods are difficult to apply to real-world
applications because of the heavy computational requirements. To facilitate the
use of a deep model under such demands, we focus on keeping the network
efficient while maintaining its performance. In detail, we design an
architecture that implements a cascading mechanism on a residual network to
boost the performance with limited resources via multi-level feature fusion. In
addition, our proposed model adopts group convolution and recursive schemes in
order to achieve extreme efficiency. We further improve the perceptual quality
of the output by employing the adversarial learning paradigm and a multi-scale
discriminator approach. The performance of our method is investigated through
extensive internal experiments and benchmarks using various datasets. Our
results show that our models outperform the recent methods with similar
complexity, for both traditional pixel-based and perception-based tasks.

36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
13
The categories of this paper are: quant-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
5
The categories of this paper are: gr-qc