# ChromaGAN: Adversarial Picture Colorization with Semantic Class Distribution

## TL;DR

ChromaGAN introduces an adversarial learning framework for grayscale image colorization that leverages semantic information, achieving realistic colorization with state-of-the-art results through self-supervised training.

## Contribution

It presents a novel adversarial approach that incorporates semantic class distributions into image colorization, improving realism and accuracy over previous methods.

## Key findings

- Achieves state-of-the-art colorization quality.
- Effectively incorporates semantic information into the colorization process.
- Operates with a fully self-supervised training strategy.

## Abstract

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.