个性化设计美学偏好建模:一种可变的自动编码器和元学习方法,用于多模式特征表示和转移优化
Chengliang Chen1, Zhiqun Gong2
1Department of Plastic Arts, Dankook University, Yongin, 16890, South Korea.
Scientific reports
|November 25, 2025
概括
这项研究引入了一个新的框架,用于个性化设计美学偏好建模,使用变异自动编码器 (VAE) 和元学习. 它准确地预测用户偏好,并快速适应,改进智能设计工具.
科学领域:
- 计算的美学 计算的美学
- 设计中的人工智能.
- 人与计算机的互动.
背景情况:
- 传统的设计评估方法缺乏个性化和通用化.
- 现有的方法难以捕捉细微的个人美学偏好.
- 在设计和内容策划方面需要适应性系统.
研究的目的:
- 为个性化设计开发一个全面的框架. 审美偏好建模.
- 将变化自动编码器 (VAE) 与元学习集成在一起,以提高偏好预测.
- 为了在各种设计领域实现强大的泛化.
主要方法:
- 利用变化自编码器 (VAE) 进行审美不确定性的概率建模.
- 采用超级学习,以最少的数据快速适应个人用户偏好.
- 集成的多模式特征提取 (视觉,文字,行为) 使用基于注意力的融合.
主要成果:
- 在审美偏好预测中获得了84.7%的准确性.
- 与传统的转移学习相比,适应要求减少了70%.
- 在六个不同的设计数据集中验证了卓越的性能.
结论:
- 拟议的框架有效地模拟了个性化的设计美学.
- 它成功地解决了个性化和跨领域通用化的挑战.
- 提供智能设计工具和自动化内容策划的实用基础.
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