This paper presents an innovative approach, called credal wrapper, to
formulating a credal set representation of model averaging for Bayesian neural
networks (BNNs) and deep ensembles, capable of improving uncertainty estimation
in classification tasks. Given a finite collection of single distributions
derived from BNNs or deep ensembles, the proposed approach extracts an upper
and a lower probability bound per class, acknowledging the epistemic
uncertainty due to the availability of a limited amount of sampled predictive
distributions. Such probability intervals over classes can be mapped on a
convex set of probabilities (a 'credal set') from which, in turn, a unique
prediction can be obtained using a transformation called 'intersection
probability transformation'. In this article, we conduct extensive experiments
on multiple out-of-distribution (OOD) detection benchmarks, encompassing
various dataset pairs (CIFAR10/100 vs SVHN/Tiny-ImageNet, CIFAR10 vs CIFAR10-C,
CIFAR100 vs CIFAR100-C and ImageNet vs ImageNet-O) and using different network
architectures (such as VGG16, Res18/50, EfficientNet B2, and ViT Base).
Compared to BNN and deep ensemble baselines, the proposed credal representation
methodology exhibits superior performance in uncertainty estimation and
achieves lower expected calibration error on OOD samples.

本文提出了一种创新方法，称为 “信任套件”，用于模型平均的贝叶斯神经网络（BNN）和深度集成的置信区间表示，能够提高分类任务中的不确定性估计。