论文标题
使用图形实例归一化的任意样式转移
Arbitrary Style Transfer using Graph Instance Normalization
论文作者
论文摘要
样式传输是图像综合任务，它在保留内容的同时将一个图像的样式应用于另一个图像。在统计方法中，自适应实例归一化（ADAIN）通过标准化特征的均值和方差应用了源图像并应用目标图像的样式。但是，每个实例的计算功能统计信息都会忽略功能之间的固有关系，因此在适合单个培训数据集的同时很难学习全局样式。在本文中，我们提出了一种新颖的可学习归一化技术，用于使用图形卷积网络，称为图形实例归一化（Grin）。通过考虑实例之间共享的类似信息，该算法使样式转移方法更加鲁棒。此外，此简单的模块还适用于其他任务，例如图像到图像翻译或域的适应。
Style transfer is the image synthesis task, which applies a style of one image to another while preserving the content. In statistical methods, the adaptive instance normalization (AdaIN) whitens the source images and applies the style of target images through normalizing the mean and variance of features. However, computing feature statistics for each instance would neglect the inherent relationship between features, so it is hard to learn global styles while fitting to the individual training dataset. In this paper, we present a novel learnable normalization technique for style transfer using graph convolutional networks, termed Graph Instance Normalization (GrIN). This algorithm makes the style transfer approach more robust by taking into account similar information shared between instances. Besides, this simple module is also applicable to other tasks like image-to-image translation or domain adaptation.
