Style Transfer Using Optimal Transport Via Wasserstein Distance

Universal style transfer has been proven to be effective through CNN models and VGG networks. However, how well to apply the algorithm’s style is a separate issue. This problem is especially evident in high-resolution images in which case the division and color at the boundary lines are more complex than low-resolution images. For the WCT model, if the image segment is smaller than the filter size, it will be blurred. High-resolution images have much larger number of small segments and the existing WCT model cannot render them clearly. This paper proposes two methods. It uses the Wasserstein distance-based optimal transport so that the resulting style distribution is the same up to the secondary statistics when a style image is applied to the content image, and proposes collaborative distillation, a method to overcome the encoder and decoder dependency of the WCT module. We propose a module that combines these two methods to apply subtle style transfer even to high-resolution images.