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

2022-08-03 01:02:12
Worasait Suwannik
arXiv_CV
       
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.

Abstract (translated)

URL

https://arxiv.org/abs/2208.04140

PDF

https://arxiv.org/pdf/2208.04140.pdf

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