Image Style Transfer and Content-Style Disentanglement
Carnegie Mellon University（卡内基梅隆大学）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
