We propose StyleBank, which is composed of multiple convolution filter banks and each filter bank explicitly represents one style, for neural image style transfer. To transfer an image to a specific style, the corresponding filter bank is operated on top of the intermediate feature embedding produced by a single auto-encoder. The StyleBank and the auto-encoder are jointly learnt, where the learning is conducted in such a way that the auto-encoder does not encode any style information thanks to the flexibility introduced by the explicit filter bank representation. It also enables us to conduct incremental learning to add a new image style by learning a new filter bank while holding the auto-encoder fixed. The explicit style representation along with the flexible network design enables us to fuse styles at not only the image level, but also the region level. Our method is the first style transfer network that links back to traditional texton mapping methods, and hence provides new understanding on neural style transfer. Our method is easy to train, runs in real-time, and produces results that qualitatively better or at least comparable to existing methods.

我们提出了StyleBank，它由多个卷积滤波器组成，每个滤波器组明确表示一个样式，用于神经图像风格转移。通过显式滤波器组表示的灵活网络设计，我们的方法可以在图像或区域级别上融合样式，并支持增量学习。与现有方法相比，我们的方法易于训练，运行速度快，输出结果定性上至少与现有方法相当甚至更好，并且是连接传统的纹理映射方法的第一个风格转移网络，从而提供了新的关于神经风格转移的理解。

StyleBank: 神经图像风格转移的明确表示