论文标题
使用Cyclegan和随机生成的数据集的黑色和白色轮廓图像的样式转移
Style Transfer of Black and White Silhouette Images using CycleGAN and a Randomly Generated Dataset
论文作者
论文摘要
Cyclegan可用于将艺术风格转移到图像上。它不需要成对的源和风格化的图像来训练模型。利用这一优势，我们建议使用随机生成的数据来训练机器学习模型，该模型可以将传统的艺术风格转移到黑色和白色轮廓图像。结果明显比以前的神经风格转移方法更好。但是，有一些需要改进的领域，例如从转化的图像中删除文物和尖峰。
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.
