PriorProbe: Recovering Individual-Level Priors for Personalizing Neural Networks in Facial Expression Recognition
This addresses the problem of personalizing neural networks for individual users in facial expression recognition, offering an incremental improvement by providing a more accurate method to recover priors compared to existing approaches.
The paper tackled the challenge of accurately eliciting individual-level cognitive priors for personalizing neural networks in facial expression recognition, and introduced PriorProbe, which recovered fine-grained priors and yielded substantial performance gains, outperforming the neural network alone and alternative priors while preserving inference on ground-truth labels.
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.