期刊论文、会议论文

Liu, Q.

英文

Advances in Intelligent Systems and Computing

2194-5357

2021

1143

375-382

9th International Conference on Computer Engineering and Networks, CENet2019

Proceedings of the 9th International Conference on Computer Engineering and Networks

18 October 2019 through 20 October 2019

Qi Liu<&wdkj&>Xiaodong Liu<&wdkj&>Lang Li<&wdkj&>Huiyu Zhou<&wdkj&>Hui-Huang Zhao

Springer, Singapore

978-981-15-3752-3

10.1007/978-981-15-3753-0_36

This work is supported by the Hunan Provincial Natural Science Foundation of China (No. 2019JJ40005), the Science and Technology Plan Project of Hunan Province (No. 2016TP1020), the General Scientific Research Fund of Hunan Provincial Education Department (No. 17C0223), the Double First-Class University Project of Hunan Province (No. Xiangjiaotong [2018]469), and Postgraduate Research and Innovation Projects of Hunan Province (No. Xiangjiaotong [2019]248–998). Hengyang guided science and technology projects and application-oriented special disciplines (No. Hengkefa [2018]60–31).

本校为第一机构

Portrait style transfer is a hot and practical direction for in-depth learning. As a deep learning model, Generative Adversarial Networks (GANs) have been widely used in image style conversion. We study Generative Adversarial Networks as a solution to the portrait style transfer problem. Here, we use GANs to recognize facial features. With large training in the conversion from plain to cosmetic drawings, this algorithm can make up the plain faces better intelligently. The experimental results provide the representation of facial image features ...