  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.
