现代信息科技2024，Vol.8Issue(4)：79-83,87,6.DOI:10.19850/j.cnki.2096-4706.2024.04.016
基于改进的生成对抗网络的动漫头像生成算法
Animation Head Sculpture Generation Algorithm Based on Improved Generative Adversarial Networks
孙慧康 1彭开阳2
作者信息
- 1. 江西理工大学 软件工程学院,江西 南昌 330013
- 2. 中国电信股份有限公司宣城分公司,安徽 宣城 242000
- 折叠
摘要
Abstract
In view of the problems of training instability,poor diversity of generated samples,poor effect on local details of characters and low quality of samples generated in most of the Generative Adversarial Networks on generation of the animation head sculptures,this paper constructs a distance penalty generator target function by using conditional entropy,and an improved model MGAN-ED is proposed combined with Attention Mechanism.The model mainly includes a generator integrated with multi-scale attention feature extraction unit and a multi-scale discriminator.The GAM and FID are used to evaluate the model.The experimental results show that the model can effectively solve the problem of pattern collapse,and the local details of the generated image are clearer and the quality of the generated samples is higher.关键词
生成对抗网络/图像生成/多尺度特征/残差结构/注意力机制Key words
Generative Adversarial Networks/image generation/multi-scale feature/residual structure/Attention Mechanism分类
信息技术与安全科学引用本文复制引用
孙慧康,彭开阳..基于改进的生成对抗网络的动漫头像生成算法[J].现代信息科技,2024,8(4):79-83,87,6.
