The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks
(CNNs) in creating artistic imagery by separating and recombining image content and style …

Recent research endeavors have shown the potential of using feed-forward convolutional
neural networks to accomplish fast style transfer for images. In this work, we take one step …

Convolutional neural networks (CNNs) have proven highly effective at image synthesis and
style transfer. For most users, however, using them as tools can be a challenging task due to …

Manually re-drawing an image in a certain artistic style takes a professional artist a long
time. Doing this for a video sequence single-handedly is beyond imagination. We present …

Neural Style Transfer based on Convolutional Neural Networks (CNN) aims to synthesize a
new image that retains the high-level structure of a content image, rendered in the low-level …

To what extent is the success of deep visualization due to the training? Could we do deep
visualization using untrained, random weight networks? To address this issue, we explore …

Recent studies have made tremendous progress in neural style transfer (NST) and various
methods have been advanced. However, evaluating and improving the stylization quality …

The deep learning method is widely used in computer vision tasks with large-scale
annotated datasets. However, obtaining such datasets in most directions of the vision based …

Gatys et al.(2015) showed that pair-wise products of features in a convolutional network are
a very effective representation of image textures. We propose a simple modification to that …

为推进基于深度学**的图像风格迁移的技术研究, 对目前基于深度学**的图像风格迁移的主要
方法和代表性工作进行了归纳与探讨. 回顾了非参数的图像风格迁移, 详细介绍了目前主要的 …