| Title | Image Style Transfer Based on Generative Adversarial Network |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Zhao, Li, Jiao, Yan, Chen, Jie, Zhao, Ruixia |
| Conference Name | 2021 International Conference on Computer Network, Electronic and Automation (ICCNEA) |
| Keywords | Automation, convolution, Deconvolution, Deep Learning, encoder, feature extraction, Force, Generative Adversarial Learning, generative adversarial network, generative adversarial networks, Image coding, Metrics, pubcrawl, resilience, Resiliency, Scalability, style transfer, Variational Auto-encoder |
| Abstract | Image style transfer refers to the transformation of the style of image, so that the image details are retained to the maximum extent while the style is transferred. Aiming at the problem of low clarity of style transfer images generated by CycleGAN network, this paper improves the CycleGAN network. In this paper, the network model of auto-encoder and variational auto-encoder is added to the structure. The encoding part of the auto-encoder is used to extract image content features, and the variational auto-encoder is used to extract style features. At the same time, the generating network of the model in this paper uses first to adjust the image size and then perform the convolution operation to replace the traditional deconvolution operation. The discriminating network uses a multi-scale discriminator to force the samples generated by the generating network to be more realistic and approximate the target image, so as to improve the effect of image style transfer. |
| DOI | 10.1109/ICCNEA53019.2021.00050 |
| Citation Key | zhao_image_2021 |
Image Style Transfer Based on Generative Adversarial Network