Uncertainty Regularized Evidential Regression
Kai Ye Tiejin Chen Hua Wei Liang Zhan
The Evidential Regression Network (ERN) represents a novel approach that
integrates deep learning with Dempster-Shafer's theory to predict a target and
quantify the associated uncertainty. Guided by the underlying theory, specific
activation functions must be employed to enforce non-negative values, which is
a constraint that compromises model performance by limiting its ability to
learn from all samples. This paper provides a theoretical analysis of this
limitation and introduces an improvement to overcome it. Initially, we define
the region where the models can't effectively learn from the samples. Following
this, we thoroughly analyze the ERN and investigate this constraint. Leveraging
the insights from our analysis, we address the limitation by introducing a
novel regularization term that empowers the ERN to learn from the whole
training set. Our extensive experiments substantiate our theoretical findings
and demonstrate the effectiveness of the proposed solution.