Bayesian neural networks (BNNs) promise improved generalization under
covariate shift by providing principled probabilistic representations of
epistemic uncertainty. However, weight-based BNNs often struggle with high
computational complexity of large-scale architectures and datasets. Node-based
BNNs have recently been introduced as scalable alternatives, which induce
epistemic uncertainty by multiplying each hidden node with latent random
variables, while learning a point-estimate of the weights. In this paper, we
interpret these latent noise variables as implicit representations of simple
and domain-agnostic data perturbations during training, producing BNNs that
perform well under covariate shift due to input corruptions. We observe that
the diversity of the implicit corruptions depends on the entropy of the latent
variables, and propose a straightforward approach to increase the entropy of
these variables during training. We evaluate the method on out-of-distribution
image classification benchmarks, and show improved uncertainty estimation of
node-based BNNs under covariate shift due to input perturbations. As a side
effect, the method also provides robustness against noisy training labels.

本文介绍一种基于节点的贝叶斯神经网络模型，通过增加隐变量将每个隐藏层节点与潜在随机变量相乘，以此模拟不同类型的数据扰动。通过增加隐变量的熵，实现了在协变量转移下，输入数据发生变化时提高隐式置信度的效果，同时还具备了对嘈杂训练数据具有鲁棒性的能力。