Arbitrary Style Transfer with Deep Feature Reshuffle

CVPR 2018 Shuyang GuCongliang ChenJing LiaoLu Yuan

This paper introduces a novel method by reshuffling deep features (i.e., permuting the spacial locations of a feature map) of the style image for arbitrary style transfer. We theoretically prove that our new style loss based on reshuffle connects both global and local style losses respectively used by most parametric and non-parametric neural style transfer methods... (read more)

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