Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Compression Artifacts Removal Using Convolutional Neural Networks   This paper shows that it is possible to train large and deep convolutional
neural networks (CNN) for JPEG compression artifacts reduction, and that such
networks can provide significantly better reconstruction quality compared to
previously used smaller networks as well as to any other state-of-the-art
methods. We were able to train networks with 8 layers in a single step and in
relatively short time by combining residual learning, skip architecture, and
symmetric weight initialization. We provide further insights into convolution
networks for JPEG artifact reduction by evaluating three different objectives,
generalization with respect to training dataset size, and generalization with
respect to JPEG quality level.

29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg