通过新方法精准提取个人认知先验，提升面部表情识别模型个性化性能。
PriorProbe: Recovering Individual-Level Priors for Personalizing Neural Networks in Facial Expression Recognition
- 基于人参与的马尔可夫链蒙特卡洛方法，精准恢复个体级先验
- 在模糊刺激上使模型准确率显著提升，优于纯神经网络和旧先验
- 方法通用可解释，适合需要个性化推理的场景
将个体认知先验融入神经网络是实现个性化的重要路径，但准确获取这些先验仍具挑战：现有方法或无法唯一识别先验，或引入系统性偏差。本文提出 PriorProbe，一种基于人参与的马尔可夫链蒙特卡洛（MCMC with People）的新提取方法，用于恢复细粒度、个体特定的先验。聚焦于面部表情识别任务，我们对每位参与者应用 PriorProbe，测试将恢复的先验与先进神经网络结合后，是否能提升其对模糊刺激的分类预测能力。结果表明，由 PriorProbe 得到的先验带来显著性能提升，优于仅使用神经网络或其它先验来源，同时保持了网络对真实标签的推理能力。这些结果证明，PriorProbe 为深度神经网络个性化提供了一个通用且可解释的框架。
原文摘要 · Abstract (English)
Incorporating individual-level cognitive priors offers an important route to personalizing neural networks, yet accurately eliciting such priors remains challenging: existing methods either fail to uniquely identify them or introduce systematic biases. Here, we introduce PriorProbe, a novel elicitation approach grounded in Markov Chain Monte Carlo with People that recovers fine-grained, individual-specific priors. Focusing on a facial expression recognition task, we apply PriorProbe to individual participants and test whether integrating the recovered priors with a state-of-the-art neural network improves its ability to predict an individual's classification on ambiguous stimuli. The PriorProbe-derived priors yield substantial performance gains, outperforming both the neural network alone and alternative sources of priors, while preserving the network's inference on ground-truth labels. Together, these results demonstrate that PriorProbe provides a general and interpretable framework for personalizing deep neural networks.
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