Artistic style transfer aims to create new artistic images by rendering a
given photograph with the target artistic style. Existing methods learn styles
simply based on global statistics or local patches, lacking careful
consideration of the drawing process in practice. Consequently, the stylization
results either fail to capture abundant and diversified local style patterns,
or contain undesired semantic information of the style image and deviate from
the global style distribution. To address this issue, we imitate the drawing
process of humans and propose a Two-Stage Statistics-Aware Transformation
(TSSAT) module, which first builds the global style foundation by aligning the
global statistics of content and style features and then further enriches local
style details by swapping the local statistics (instead of local features) in a
patch-wise manner, significantly improving the stylization effects. Moreover,
to further enhance both content and style representations, we introduce two
novel losses: an attention-based content loss and a patch-based style loss,
where the former enables better content preservation by enforcing the semantic
relation in the content image to be retained during stylization, and the latter
focuses on increasing the local style similarity between the style and stylized
images. Extensive qualitative and quantitative experiments verify the
effectiveness of our method.

通过模拟人类绘画过程，提出了两阶段统计感知转换（TSSAT）模块来改善艺术风格迁移的效果，并引入了基于注意力的内容损失和基于补丁的风格损失来增强内容和风格的表示。大量的定性和定量实验证实了我们方法的有效性。

TSSAT: 艺术风格转换的两阶段统计感知变换

TSSAT: Two-Stage Statistics-Aware Transformation for Artistic Style  Transfer

The key challenge in photorealistic style transfer is that an algorithm
should faithfully transfer the style of a reference photo to a content photo
while the generated image should look like one captured by a camera. Although
several photorealistic style transfer algorithms have been proposed, they need
to rely on post- and/or pre-processing to make the generated images look
photorealistic. If we disable the additional processing, these algorithms would
fail to produce plausible photorealistic stylization in terms of detail
preservation and photorealism. In this work, we propose an effective solution
to these issues. Our method consists of a construction step (C-step) to build a
photorealistic stylization network and a pruning step (P-step) for
acceleration. In the C-step, we propose a dense auto-encoder named PhotoNet
based on a carefully designed pre-analysis. PhotoNet integrates a feature
aggregation module (BFA) and instance normalized skip links (INSL). To generate
faithful stylization, we introduce multiple style transfer modules in the
decoder and INSLs. PhotoNet significantly outperforms existing algorithms in
terms of both efficiency and effectiveness. In the P-step, we adopt a neural
architecture search method to accelerate PhotoNet. We propose an automatic
network pruning framework in the manner of teacher-student learning for
photorealistic stylization. The network architecture named PhotoNAS resulted
from the search achieves significant acceleration over PhotoNet while keeping
the stylization effects almost intact. We conduct extensive experiments on both
image and video transfer. The results show that our method can produce
favorable results while achieving 20-30 times acceleration in comparison with
the existing state-of-the-art approaches. It is worth noting that the proposed
algorithm accomplishes better performance without any pre- or post-processing.

本文提出了一种基于 PhotoNet 和神经架构搜索的相机级画风快速迁移算法，在保持细节保留和逼真性的同时，相比当前最先进的方法可以实现 20-30 倍的加速，而无需进行预处理或后处理。