Bayesian neural networks (BNN) can estimate the uncertainty in predictions,
as opposed to non-Bayesian neural networks (NNs). However, BNNs have been far
less widely used than non-Bayesian NNs in practice since they need iterative NN
executions to predict a result for one data, and it gives rise to prohibitive
computational cost. This computational burden is a critical problem when
processing data streams with low-latency. To address this problem, we propose a
novel model VQ-BNN, which approximates BNN inference for data streams. In order
to reduce the computational burden, VQ-BNN inference predicts NN only once and
compensates the result with previously memorized predictions. To be specific,
VQ-BNN inference for data streams is given by temporal exponential smoothing of
recent predictions. The computational cost of this model is almost the same as
that of non-Bayesian NNs. Experiments including semantic segmentation on
real-world data show that this model performs significantly faster than BNNs
while estimating predictive results comparable to or superior to the results of
BNNs.

本研究提出了一种名为 VQ-BNN 的新模型，可以快速准确预测数据流的预测结果，同时避免了通常 Bayes 网络需要耗费大量计算资源的问题。