Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Generative Adversarial Compression Artifact Removal   Compression artifacts arise in images whenever a lossy compression algorithm
is applied. These artifacts eliminate details present in the original image, or
add noise and small structures; because of these effects they make images less
pleasant for the human eye, and may also lead to decreased performance of
computer vision algorithms such as object detectors. To eliminate such
artifacts, when decompressing an image, it is required to recover the original
image from a disturbed version. To this end, we present a feed-forward fully
convolutional residual network model trained using a generative adversarial
framework. To provide a baseline, we show that our model can be also trained
optimizing the Structural Similarity (SSIM), which is a better loss with
respect to the simpler Mean Squared Error (MSE). Our GAN is able to produce
images with more photorealistic details than MSE or SSIM based networks.
Moreover we show that our approach can be used as a pre-processing step for
object detection in case images are degraded by compression to a point that
state-of-the art detectors fail. In this task, our GAN method obtains better
performance than MSE or SSIM trained networks.

30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat