Abstract
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. This simple idea can effectively address the
challenging issues in existing style transfer methods. On
one hand, it can avoid distortions in local style patterns,
and allow semantic-level transfer, compared with neural
parametric methods. On the other hand, it can preserve
globally similar appearance to the style image, and avoid
wash-out artifacts, compared with neural non-parametric
methods. Based on the proposed loss, we also present a progressive feature-domain optimization approach. The experiments show that our method is widely applicable to various
styles, and produces better quality than existing methods