Uncertainty estimation is increasingly attractive for improving the
reliability of neural networks. In this work, we present novel credal-set
interval neural networks (CreINNs) designed for classification tasks. CreINNs
preserve the traditional interval neural network structure, capturing weight
uncertainty through deterministic intervals, while forecasting credal sets
using the mathematical framework of probability intervals. Experimental
validations on an out-of-distribution detection benchmark (CIFAR10 vs SVHN)
showcase that CreINNs outperform epistemic uncertainty estimation when compared
to variational Bayesian neural networks (BNNs) and deep ensembles (DEs).
Furthermore, CreINNs exhibit a notable reduction in computational complexity
compared to variational BNNs and demonstrate smaller model sizes than DEs.

本研究介绍了一种用于分类任务的全新可信区间神经网络（CreINNs），通过确定性区间捕获权重不确定性，同时使用概率区间的数学框架预测区间集合；对 CIFAR10 与 SVHN 的异分布检测基准进行的实验证实表明，与变分贝叶斯神经网络（BNNs）和深度集成（DEs）相比，CreINNs 在对认知不确定性估计方面表现优越，并且 CreINNs 的计算复杂度大幅降低，模型尺寸较 DEs 更小。