Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: S-Net: A Scalable Convolutional Neural Network for JPEG Compression
  Artifact Reduction   Recent studies have used deep residual convolutional neural networks (CNNs)
for JPEG compression artifact reduction. This study proposes a scalable CNN
called S-Net. Our approach effectively adjusts the network scale dynamically in
a multitask system for real-time operation with little performance loss. It
offers a simple and direct technique to evaluate the performance gains obtained
with increasing network depth, and it is helpful for removing redundant network
layers to maximize the network efficiency. We implement our architecture using
the Keras framework with the TensorFlow backend on an NVIDIA K80 GPU server. We
train our models on the DIV2K dataset and evaluate their performance on public
benchmark datasets. To validate the generality and universality of the proposed
method, we created and utilized a new dataset, called WIN143, for
over-processed images evaluation. Experimental results indicate that our
proposed approach outperforms other CNN-based methods and achieves
state-of-the-art performance.

11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
18
The categories of this paper are: econ