| Title | Style Transfer Analysis Based on Generative Adversarial Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Bo, Xihao, Jing, Xiaoyang, Yang, Xiaojian |
| Conference Name | 2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI) |
| Date Published | sep |
| Keywords | Deep Learning, gan, generative adversarial networks, Metrics, Neural Network, Neural networks, neural style transfer, object detection, process control, Production, pubcrawl, resilience, Resiliency, Scalability, social networking (online), style transfer, visualization |
| Abstract | Style transfer means using a neural network to extract the content of one image and the style of the other image. The two are combined to get the final result, broadly applied in social communication, animation production, entertainment items. Using style transfer, users can share and exchange images; painters can create specific art styles more readily with less creation cost and production time. Therefore, style transfer is widely concerned recently due to its various and valuable applications. In the past few years, the paper reviews style transfer and chooses three representative works to analyze in detail and contrast with each other, including StyleGAN, CycleGAN, and TL-GAN. Moreover, what function an ideal model of style transfer should realize is discussed. Compared with such a model, potential problems and prospects of different methods to achieve style transfer are listed. A couple of solutions to these drawbacks are given in the end. |
| DOI | 10.1109/CEI52496.2021.9574507 |
| Citation Key | bo_style_2021 |
Style Transfer Analysis Based on Generative Adversarial Networks