| Title | Analysis of Neural Style Transfer Based on Generative Adversarial Network |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Peng, Cheng, Xu, Chenning, Zhu, Yincheng |
| Conference Name | 2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI) |
| Date Published | sep |
| Keywords | ART, Computer science, Conferences, Deep Learning, gan, generative adversarial networks, Metrics, Neural Network, neural style transfer, Oils, pubcrawl, resilience, Resiliency, Scalability, Transforms |
| Abstract | The goal of neural style transfer is to transform images by the deep learning method, such as changing oil paintings into sketch-style images. The Generative Adversarial Network (GAN) has made remarkable achievements in neural style transfer in recent years. At first, this paper introduces three typical neural style transfer methods, including StyleGAN, StarGAN, and Transparent Latent GAN (TL-GAN). Then, we discuss the advantages and disadvantages of these models, including the quality of the feature axis, the scale, and the model's interpretability. In addition, as the core of this paper, we put forward innovative improvements to the above models, including how to fully exploit the advantages of the above three models to derive a better style conversion model. |
| DOI | 10.1109/CEI52496.2021.9574603 |
| Citation Key | peng_analysis_2021 |
Analysis of Neural Style Transfer Based on Generative Adversarial Network