论文标题
样式是功能的分布
Style is a Distribution of Features
论文作者
论文摘要
神经风格转移（NST）是一种强大的图像生成技术，它使用卷积神经网络（CNN）将一个图像的内容与另一个图像的内容合并。 NST的当代方法使用CNN功能的第一阶或二阶统计，以相对较少的计算成本实现转移。但是，这些方法无法完全从CNN的功能中提取样式。我们提出了一种用于样式传输的新算法，该算法通过重新定义样式损失，将样式损失从功能分布之间重新定义，从而从功能中充分提取样式。因此，我们设定了样式转移质量的新标准。此外，我们陈述了NST的两个重要解释。首先是Li等人的重新强调，该重点只是样式只是特征的分布。第二个指出，NST是一种生成对抗网络（GAN）问题。
Neural style transfer (NST) is a powerful image generation technique that uses a convolutional neural network (CNN) to merge the content of one image with the style of another. Contemporary methods of NST use first or second order statistics of the CNN's features to achieve transfers with relatively little computational cost. However, these methods cannot fully extract the style from the CNN's features. We present a new algorithm for style transfer that fully extracts the style from the features by redefining the style loss as the Wasserstein distance between the distribution of features. Thus, we set a new standard in style transfer quality. In addition, we state two important interpretations of NST. The first is a re-emphasis from Li et al., which states that style is simply the distribution of features. The second states that NST is a type of generative adversarial network (GAN) problem.
