# Grayscale Image Colorization with GAN and CycleGAN in Different Image Domain

arXiv: https://arxiv.org/abs/2401.11425 · HTML: https://mlsanity.com/paper/2401.11425/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 21 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop](https://mlsanity.com/paper/1506.03365/) (2015)
- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [Learning Diverse Image Colorization](https://mlsanity.com/paper/1612.01958/) (2016)
- [Unsupervised Diverse Colorization via Generative Adversarial Networks](https://mlsanity.com/paper/1702.06674/) (2017)
- [Image Colorization with Generative Adversarial Networks](https://mlsanity.com/paper/1803.05400/) (2018)

## Abstract (arXiv)

Automatic colorization of grayscale image has been a challenging task. Previous research have applied supervised methods in conquering this problem [ 1]. In this paper, we reproduces a GAN-based coloring model, and experiments one of its variant. We also proposed a CycleGAN based model and experiments those methods on various datasets. The result shows that the proposed CycleGAN model does well in human-face coloring and comic coloring, but lack the ability to diverse colorization.