区分人脸年龄估计中模型与人类数据的不确定性，提升预测可信度。
Disentangling Model and Human Data Uncertainty in Apparent Facial Age Estimation
- 用贝叶斯神经网络分离认知不确定性和固有随机性
- 数据越少，模型认知不确定性越大，但固有不确定性稳定
- 基于APPA-REAL数据集，适合研究可靠性评估的开发者
从人脸图像估计外在年龄具有挑战性，源于感知主观性与数据内在变异性。本文探究不确定性估计，将不确定性归因于知识不足（认知型）或固有噪声（随机型）。基于APPA-REAL数据集，使用三种贝叶斯神经网络近似方法（MC-DropConnect、Flipout、Deep Ensembles），在不同规模数据上训练，并利用该数据集中提供的真人随机不确定性监督信号。各模型输出预测年龄及对应的随机与认知不确定性。结果表明：固有不确定性随数据量变化保持稳定，而认知不确定性随训练数据减少而增加。这证明了在人脸年龄估计中可量化不同来源的不确定性。
原文摘要 · Abstract (English)
Estimating the apparent age of individuals from facial images is challenging due to the subjective nature of perception and the inherent variability of the data. We investigate the role of uncertainty estimation, attributing uncertainty jointly to a lack of knowledge (epistemic) or inherent noise/chance (aleatoric). Leveraging the APPA-REAL dataset, we train Bayesian Neural Networks on datasets of varying sizes using three BNN approximations: MC-DropConnect, Flipout, and Deep Ensembles using supervision on human aleatoric uncertainty available in the APPA-REAL dataset. Each model outputs both the predicted apparent age and the amount of aleatoric and epistemic uncertainty. Our results confirm the hypothesis that the inherent aleatoric uncertainty remains stable across dataset sizes, while epistemic uncertainty increases as training data decreases. These findings demonstrate that different sources of uncertainty can be quantified in face age estimation.
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