Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Image Distortion Detection using Convolutional Neural Network   Image distortion classification and detection is an important task in many
applications. For example when compressing images, if we know the exact
location of the distortion, then it is possible to re-compress images by
adjusting the local compression level dynamically. In this paper, we address
the problem of detecting the distortion region and classifying the distortion
type of a given image. We show that our model significantly outperforms the
state-of-the-art distortion classifier, and report accurate detection results
for the first time. We expect that such results prove the usefulness of our
approach in many potential applications such as image compression or distortion
restoration.

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The categories of this paper are: cond-mat
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The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
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The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc