Deep neural networks lack interpretability and tend to be overconfident,
which poses a serious problem in safety-critical applications like autonomous
driving, medical imaging, or machine vision tasks with high demands on
reliability. Quantifying the predictive uncertainty is a promising endeavour to
open up the use of deep neural networks for such applications. Unfortunately,
current available methods are computationally expensive. In this work, we
present a novel approach for efficient and reliable uncertainty estimation
which we call Deep Uncertainty Distillation using Ensembles for Segmentation
(DUDES). DUDES applies student-teacher distillation with a Deep Ensemble to
accurately approximate predictive uncertainties with a single forward pass
while maintaining simplicity and adaptability. Experimentally, DUDES accurately
captures predictive uncertainties without sacrificing performance on the
segmentation task and indicates impressive capabilities of identifying wrongly
classified pixels and out-of-domain samples on the Cityscapes dataset. With
DUDES, we manage to simultaneously simplify and outperform previous work on
Deep Ensemble-based Uncertainty Distillation.

本研究介绍了一种新方法 Deep Uncertainty Distillation using Ensembles for Segmentation (DUDES)，它能够高效地进行深度神经网络的预测不确定性估计，而不会牺牲性能，以解决自动驾驶、医学图像和机器视觉应用中模型可靠性不足的问题。