  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.
