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Image Style Transfer and Content-Style Disentanglement

2021-11-25 01:26:45
Sailun Xu, Jiazhi Zhang, Jiamei Liu
arXiv_CV
     
Abstract

We propose a way of learning disentangled content-style representation of image, allowing us to extrapolate images to any style as well as interpolate between any pair of styles. By augmenting data set in a supervised setting and imposing triplet loss, we ensure the separation of information encoded by content and style representation. We also make use of cycle-consistency loss to guarantee that images could be reconstructed faithfully by their representation.

Abstract (translated)

URL

https://arxiv.org/abs/2111.15624

PDF

https://arxiv.org/pdf/2111.15624.pdf

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