Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Image Inpainting via Generative Multi-column Convolutional Neural
  Networks   In this paper, we propose a generative multi-column network for image
inpainting. This network synthesizes different image components in a parallel
manner within one stage. To better characterize global structures, we design a
confidence-driven reconstruction loss while an implicit diversified MRF
regularization is adopted to enhance local details. The multi-column network
combined with the reconstruction and MRF loss propagates local and global
information derived from context to the target inpainting regions. Extensive
experiments on challenging street view, face, natural objects and scenes
manifest that our method produces visual compelling results even without
previously common post-processing.

34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
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The categories of this paper are: bayes-an
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg