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.

本文提出了一种新颖的神经风格迁移方法，该方法通过重新调整风格图像的深层特征(即，置换特征映射的空间位置)来减少样式图像上的失真，同时能够允许语义级别的转移。同时，基于提出的损失，文中还提出了一种渐进式特征域优化方法。实验表明，该方法适用于各种风格的图像，并产生比现有方法更好的效果。

利用深度特征重排进行任意风格转移