In this paper, we present a method which combines the flexibility of the neural algorithm of artistic style with the speed of fast style transfer networks to allow real-time stylization using any content/style image pair. We build upon recent work leveraging conditional instance normalization for multi-style transfer networks by learning to predict the conditional instance normalization parameters directly from a style image. The model is successfully trained on a corpus of roughly 80,000 paintings and is able to generalize to paintings previously unobserved. We demonstrate that the learned embedding space is smooth and contains a rich structure and organizes semantic information associated with paintings in an entirely unsupervised manner.

本文提出一种方法，将艺术风格的神经算法的灵活性与快速风格转移网络的速度相结合，利用任意内容/风格图像对进行实时样式化。通过学习直接从样式图像预测条件实例归一化参数，在多风格转移网络中使用条件实例归一化对最近工作进行了改进。该模型成功地在大约8万幅绘画作品上进行了训练，并能够推广到以前未观察到的作品。我们证明，学习到的嵌入空间是平滑的，并在整个非监督学习过程中包含了与绘画相关的丰富结构和组织语义信息。

探索实时任意神经艺术风格化网络的结构