CUPID: A Plug-in Framework for Joint Aleatoric and Epistemic Uncertainty Estimation with a Single Model

• URL: http://arxiv.org/abs/2603.10745v1
• Date: Wed, 11 Mar 2026 13:18:10 GMT
• Title: CUPID: A Plug-in Framework for Joint Aleatoric and Epistemic Uncertainty Estimation with a Single Model
• Abstract summary: CUPID (Comprehensive Uncertainty Plug-in estImation moDel) is a general-purpose module that jointly estimates aleatoric and epistemic uncertainty without modifying or retraining the base model.<n>We evaluate CUPID across a range of tasks, including classification, regression, and out-of-distribution detection.<n>The results show that CUPID consistently delivers competitive performance while offering layer-wise insights into the origins of uncertainty.
• Score: 1.6855257501750571
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate estimation of uncertainty in deep learning is critical for deploying models in high-stakes domains such as medical diagnosis and autonomous decision-making, where overconfident predictions can lead to harmful outcomes. In practice, understanding the reason behind a model's uncertainty and the type of uncertainty it represents can support risk-aware decisions, enhance user trust, and guide additional data collection. However, many existing methods only address a single type of uncertainty or require modifications and retraining of the base model, making them difficult to adopt in real-world systems. We introduce CUPID (Comprehensive Uncertainty Plug-in estImation moDel), a general-purpose module that jointly estimates aleatoric and epistemic uncertainty without modifying or retraining the base model. CUPID can be flexibly inserted into any layer of a pretrained network. It models aleatoric uncertainty through a learned Bayesian identity mapping and captures epistemic uncertainty by analyzing the model's internal responses to structured perturbations. We evaluate CUPID across a range of tasks, including classification, regression, and out-of-distribution detection. The results show that it consistently delivers competitive performance while offering layer-wise insights into the origins of uncertainty. By making uncertainty estimation modular, interpretable, and model-agnostic, CUPID supports more transparent and trustworthy AI. Related code and data are available at https://github.com/a-Fomalhaut-a/CUPID.

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