用变分自编码器发现：脸越美，越容易被大脑快速处理。
Beauty is in the ELBO of the Beholder: A Variational Account of Processing Fluency in Face Perception
- 用无美感标注的面部数据训练变分自编码器，发现美感可由模型对脸的重构难易度（ELBO）预测。
- 人类对597张脸的审美评分与模型重构效率高度一致，且该美感方向跨不同训练随机种子和数据集稳定存在。
- 越美的脸在形状和隐空间中越接近平均脸，支持‘美即熟悉’的理论。
面部吸引力与对称性、平均性等统计规律相关，提示美感可能源于感知的流畅性。我们通过在四个面部数据集上训练无美感监督的变分自编码器（VAE），并在芝加哥面部数据库（Chicago Face Database）的597张人脸样本上评估其表示能力，发现人类对吸引力的评分与模型在率-失真空间中的证据下界（ELBO）方向高度一致。独立学习的隐空间中存在一个跨随机初始化和训练数据强烈转移的吸引力方向。同时发现，吸引力高的面部在形状和隐空间中更接近原型。结果将经典美学理论与学习到的生成模型联系起来，为审美愉悦的加工流畅性理论提供了实证支持。
原文摘要 · Abstract (English)
Facial attractiveness has been linked to statistical regularities such as symmetry and averageness, suggesting that beauty may depend on the ease with which a face is perceived. We empirically test this hypothesis by training variational autoencoders on four face datasets without attractiveness supervision and evaluating their representations on the 597 faces from the Chicago Face Database. Across models, human attractiveness ratings closely aligns with the direction defined by the VAE evidence lower bound (ELBO) in rate-distortion space. Independently learned latent spaces contain an attractiveness direction that transfers strongly across random initializations and training data. We also find that attractive faces are more prototypical in both shape and latent space. Our results connect classic accounts of aesthetics with learned generative models and provide empirical support for a variational interpretation of the processing fluency theory of aesthetic pleasure.
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