  Uncertainty estimation is essential to make neural networks trustworthy in
real-world applications. Extensive research efforts have been made to quantify
and reduce predictive uncertainty. However, most existing works are designed
for unimodal data, whereas multi-view uncertainty estimation has not been
sufficiently investigated. Therefore, we propose a new multi-view
classification framework for better uncertainty estimation and out-of-domain
sample detection, where we associate each view with an uncertainty-aware
classifier and combine the predictions of all the views in a principled way.
The experimental results with real-world datasets demonstrate that our proposed
approach is an accurate, reliable, and well-calibrated classifier, which
predominantly outperforms the multi-view baselines tested in terms of expected
calibration error, robustness to noise, and accuracy for the in-domain sample
classification and the out-of-domain sample detection tasks.
