Style transfer aims to render the content of a given image in the graphical/artistic style of another image. The fundamental concept underlying NeuralStyle Transfer (NST) is to interpret style as a distribution in the feature space of a Convolutional Neural Network, such that a desired style can be achieved by matching its feature distribution. We show that most current implementations of that concept have important theoretical and practical limitations, as they only partially align the feature distributions. We propose a novel approach that matches the distributions more precisely, thus reproducing the desired style more faithfully, while still being computationally efficient. Specifically, we adapt the dual form of Central Moment Discrepancy (CMD), as recently proposed for domain adaptation, to minimize the difference between the target style and the feature distribution of the output image. The dual interpretation of this metric explicitly matches all higher-order centralized moments and is therefore a natural extension of existing NST methods that only take into account the first and second moments. Our experiments confirm that the strong theoretical properties also translate to visually better style transfer, and better disentangle style from semantic image content.
翻译:样式传输的目的是让另一图像的图形/ 艺术风格中给定图像的内容成为另一图像的图形/ 艺术风格。 神经系统转移( NST) 的基本概念是将风格解释为一个动态神经网络的特性空间的分布, 以便通过匹配其特性分布来达到理想的样式。 我们显示,当前大多数概念的实施都具有重要的理论和实践限制, 因为它们只是部分地匹配特性分布。 我们建议一种新颖的方法, 更精确地匹配分布, 从而更忠实地复制想要的风格, 同时仍然在计算效率上保持效率。 具体地说, 我们调整了最近为域调整提议的中央运动差异( CMD) 的双重形式, 以尽可能缩小目标样式与输出图像特征分布之间的差别。 这个参数的双重解释明确匹配所有更高顺序集中的时段, 因而是现有 NST 方法的自然延伸, 仅考虑到第一和第二时刻。 我们的实验证实, 强的理论属性还转化成视觉更好的样式转移, 并且更好地区分语义图像内容的风格。