Streamlined and Resource-Efficient Predictive Uncertainty Estimation of
Deep Ensemble Predictions via Regression
Abstract
This paper highlights the contribution of utilizing ensemble deep
learning with auto-encoders (AEs) for out-of-distribution data
detection. The key innovation is treating ensemble UQ as a regression
problem, mapping uncertainty distribution to a single model, reducing
computational demands. This approach aligns well with the ensemble of
AEs’ uncertainty distribution, making it valuable for
resource-constrained systems and rapid decision-making in computational
intelligence.