# Style Transfer of Black and White Silhouette Images using CycleGAN and a Randomly Generated Dataset

arXiv: https://arxiv.org/abs/2208.04140 · HTML: https://mlsanity.com/paper/2208.04140/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 4 references that are in the map.
- First posted on arXiv 3 Aug 2022.

## Builds on (most cited first, 3 of 3)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)

## Abstract (arXiv)

CycleGAN can be used to transfer an artistic style to an image. It does not require pairs of source and stylized images to train a model. Taking this advantage, we propose using randomly generated data to train a machine learning model that can transfer traditional art style to a black and white silhouette image. The result is noticeably better than the previous neural style transfer methods. However, there are some areas for improvement, such as removing artifacts and spikes from the transformed image.