Paper ID: 2111.15624

Image Style Transfer and Content-Style Disentanglement

Sailun Xu, Jiazhi Zhang, Jiamei Liu

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.

Submitted: Nov 25, 2021