🤖 AI Summary
This work addresses the unreliable and unstable estimation of predictive uncertainty from softmax outputs of neural network classifiers, which adversely affects downstream task performance. The authors propose a novel approach that does not rely on evidential learning losses; instead, it explicitly estimates the parameters of a Dirichlet distribution by aggregating multiple softmax outputs and combining the method of moments with optional maximum likelihood optimization. This formulation effectively decouples uncertainty modeling from classification training. Evaluated across multiple datasets, the method significantly improves the quality of uncertainty estimates and achieves superior performance in tasks such as confidence calibration and selective classification, demonstrating both robustness and practical applicability.
📝 Abstract
Neural network classifiers trained with cross-entropy loss achieve strong predictive accuracy but lack the capability to provide inherent predictive uncertainty estimates, thus requiring external techniques to obtain these estimates. In addition, softmax scores for the true class can vary substantially across independent training runs, which limits the reliability of uncertainty-based decisions in downstream tasks. Evidential Deep Learning aims to address these limitations by producing uncertainty estimates in a single pass, but evidential training is highly sensitive to design choices including loss formulation, prior regularization, and activation functions. Therefore, this work introduces an alternative Dirichlet parameter estimation strategy by applying a method of moments estimator to ensembles of softmax outputs, with an optional maximum-likelihood refinement step. This ensemble-based construction decouples uncertainty estimation from the fragile evidential loss design while also mitigating the variability of single-run cross-entropy training, producing explicit Dirichlet predictive distributions. Across multiple datasets, we show that the improved stability and predictive uncertainty behavior of these ensemble-derived Dirichlet estimates translate into stronger performance in downstream uncertainty-guided applications such as prediction confidence scoring and selective classification.