Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ChromaGAN: Adversarial Picture Colorization with Semantic Class
  Distribution   The colorization of grayscale images is an ill-posed problem, with multiple
correct solutions. In this paper, we propose an adversarial learning
colorization approach coupled with semantic information. A generative network
is used to infer the chromaticity of a given grayscale image conditioned to
semantic clues. This network is framed in an adversarial model that learns to
colorize by incorporating perceptual and semantic understanding of color and
class distributions. The model is trained via a fully self-supervised strategy.
Qualitative and quantitative results show the capacity of the proposed method
to colorize images in a realistic way achieving state-of-the-art results.

14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th