Abstract
Image style transfer is one of the computer vision applications related to deep machine learning. Since the proposal of the first online learning approach of single layered neural network called neural style, image style transferring method has been continuously improved in processing speed and style capacity. However, controlling the style strength of image has not been investigated deeply. As an early stage of research for style strength control, we propose a method of style manifold learning in image decoder which can generate unbiased style image for image style transfer.
Abstract (translated)
图像样式转移是与深度机器学习相关的计算机视觉应用之一。由于第一种单层神经网络在线学习方法被称为神经风格，因此图像样式转换方法在处理速度和样式容量方面不断提高。然而，控制图像的风格强度尚未得到深入研究。作为风格强度控制研究的早期阶段，我们提出了一种图像解码器中的风格流形学习方法，可以生成无偏见的图像风格转换图像。
URL
https://arxiv.org/abs/1807.01424
