用可解释的AI揭示美学偏好的因素
Derya Soydaner1, Johan Wagemans1
1Department of Brain and Cognition, University of Leuven (KU Leuven), Leuven, Belgium.
概括
这项研究使用机器学习 (ML) 和可解释AI (XAI) 来理解图像美学. 我们确定了影响美学偏好的关键属性,并提供了对视觉吸引力预测的见解.
科学领域:
- 计算机科学 计算机科学
- 心理学 心理学 心理学
- 人工智能是一种人工智能.
背景情况:
- 了解图像中的美学偏好是复杂的.
- 机器学习 (ML) 提供了分析视觉吸引力的潜力.
- 可解释AI (XAI) 可以提供对ML模型决策的见解.
研究的目的:
- 开发和比较用于预测图像美学分数的ML模型.
- 利用可解释的人工智能 (XAI),特别是夏普利添加式解释 (SHAP),对美学偏好进行可解释的见解.
- 研究特定属性及其相互作用在影响美学判断中的作用.
主要方法:
- 采用多个ML模型:随机森林,XGBoost,支持向量回归和多层感知器.
- 为了模型的可解释性,利用了夏普利添加式解释 (SHAP).
- 在三个基准数据集上进行实验:AADB,EVA和PARA.
主要成果:
- 证明了ML模型在预测图像美学分数方面的有效性.
- 通过SHAP分析确定了通过SHAP分析显著影响美学偏好的关键图像属性.
- 在使用SHAP分析时,在不同的ML模型中观察到一致的性能.
结论:
- 通过XAI增强的ML模型为美学研究提供了强大的框架.
- 这项研究加深了对驱动图像审美判断的属性的理解.
- 这些发现为预测和理解视觉吸引力提供了有价值的工具.
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