Visible to the public Image Style Transfer in Deep Learning Networks

TitleImage Style Transfer in Deep Learning Networks
Publication TypeConference Paper
Year of Publication2018
AuthorsLi, Y., Zhang, T., Han, X., Qi, Y.
Conference Name2018 5th International Conference on Systems and Informatics (ICSAI)
KeywordsART, artistic styles, classical style migration model, CNN, Computer vision, computer vision researchers, convolution neural network, convolutional neural nets, Deep Learning, deep learning network development process, Image color analysis, image recognition, image style transfer, images contents, learning (artificial intelligence), Neural networks, neural style transfer, Painting, Predictive Metrics, pubcrawl, Resiliency, Scalability, Semantics

Since Gatys et al. proved that the convolution neural network (CNN) can be used to generate new images with artistic styles by separating and recombining the styles and contents of images. Neural Style Transfer has attracted wide attention of computer vision researchers. This paper aims to provide an overview of the style transfer application deep learning network development process, and introduces the classical style migration model, on the basis of the research on the migration of style of the deep learning network for collecting and organizing, and put forward related to gathered during the investigation of the problem solution, finally some classical model in the image style to display and compare the results of migration.

Citation Keyli_image_2018