通过可解释的条件对抗自动编码器进行面部老化
Christos Korgialas1, Evangelia Pantraki1, Angeliki Bolari1
1Department of Informatics, Faculty of Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
Journal of imaging
|May 26, 2023
概括
本研究介绍了使用生成对抗网络 (GAN) 和可解释AI (xAI) 的可解释面部衰老框架. 通过提供可解释的反,xAI-CAAE模型增强了年龄进步和回归图像的现实性.
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:
- 面部衰老研究传统上使用生成对抗网络 (GAN).
- 现有的方法在老化过程中缺乏可解释性.
- 条件对抗自编码器 (CAAE) 是面对面的常见方法.
研究的目的:
- 提出一个可解释的面部老化框架 (xAI-CAAE).
- 将可解释的人工智能 (xAI) 方法集成到基于GAN的面部老化中.
- 通过可解释的反来提高年龄进步和回归图像的现实性.
主要方法:
- 将条件对抗自编码器 (CAAE) 与 Saliency 地图和 Shapley 附加解释等 xAI 技术结合起来.
- 使用xAI指导的培训,从歧视器到生成器进行校正反.
- 使用局部可解释的模型不可知解释 (LIME) 进行预训练的年龄分类器分析.
主要成果:
- 证明了xAI系统对图像现实性的重大贡献.
- 产生了更现实的年龄进步和年龄倒退的面部图像.
- 通过解释,为歧视者的决定提供了"理由".
结论:
- 新的xAI-CAAE框架成功地增强了面部老化的现实主义.
- 可解释的人工智能方法在面部老化中首次被使用.
- 集成xAI在生成可解释和现实的面部年龄操纵方面取得了重大进展.
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