Style transfer is the task of reproducing the semantic contents of a source image in the artistic style of a second target image. In this paper, we present NeAT, a new state-of-the art feed-forward style transfer method. We re-formulate feed-forward style transfer as image editing, rather than image generation, resulting in a model which improves over the state-of-the-art in both preserving the source content and matching the target style. An important component of our model's success is identifying and fixing "style halos", a commonly occurring artefact across many style transfer techniques. In addition to training and testing on standard datasets, we introduce the BBST-4M dataset, a new, large scale, high resolution dataset of 4M images. As a component of curating this data, we present a novel model able to classify if an image is stylistic. We use BBST-4M to improve and measure the generalization of NeAT across a huge variety of styles. Not only does NeAT offer state-of-the-art quality and generalization, it is designed and trained for fast inference at high resolution.

本文介绍了NeAT，一种新的前馈风格转移方法，将前馈风格转移重新定义为图像编辑而不是图像生成，以在保留源内容和匹配目标风格方面改进最新技术。在介绍BBST-4M数据集的同时，提出了一种新的模型，用于对图像进行风格分类，并使用该数据集将NeAT的推广和衡量提高到了另一个水平。NeAT不仅在质量和推广方面处于最新状态，而且针对高分辨率的快速推断进行了设计和训练。

NeAT：用于美丽风格转换的神经艺术追踪