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

ABSTRACT: 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.
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.
Style Transfer of Black and White Silhouette Images using CycleGAN and a Randomly Generated Dataset
http://arxiv.org/abs/2208.04140v1