解开GAN的潜伏空间,用于语义面部编辑
Yongjie Niu1,2, Mingquan Zhou1, Zhan Li1
1School of Information Science and Technology, Northwest University, Xi'an, China.
PloS one
|October 26, 2023
概括
这项研究引入了一个新的两阶段框架,用于在生成模型中进行无监督,无纠的图像编辑. 该方法提高了属性编辑的解释性,并减少了不需要的属性纠,以实现更清晰的图像处理.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 解对于可解释的图像编辑至关重要.
- 现有的方法与纠的编辑方向扎,缺乏协同的方向发现和解.
研究的目的:
- 为了呈现一个无监督的,无模型的框架来解开图像编辑.
- 解决当前关于属性纠和单独的方向发现/解的研究中的局限性.
主要方法:
- 建议采用两阶段的培训方法.
- 这种方法发现语义编辑方向,扰乱方向向量,并使用惩罚机制来解开纠.
主要成果:
- 该框架在生成的图像中实现了卓越的解释性,解和区分性.
- 定性和定量实验表明,与现有方法相比,性能优于现有方法.
结论:
- 拟议的两阶段框架有效地实现了脱而出的图像编辑.
- 这种方法可以更好地控制图像属性,如年龄和面部表情.
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