  Neural network ensembles, such as Bayesian neural networks (BNNs), have shown
success in the areas of uncertainty estimation and robustness. However, a
crucial challenge prohibits their use in practice. BNNs require a large number
of predictions to produce reliable results, leading to a significant increase
in computational cost. To alleviate this issue, we propose spatial smoothing, a
method that spatially ensembles neighboring feature map points of convolutional
neural networks. By simply adding a few blur layers to the models, we
empirically show that spatial smoothing improves accuracy, uncertainty
estimation, and robustness of BNNs across a whole range of ensemble sizes. In
particular, BNNs incorporating spatial smoothing achieve high predictive
performance merely with a handful of ensembles. Moreover, this method also can
be applied to canonical deterministic neural networks to improve the
performances. A number of evidences suggest that the improvements can be
attributed to the stabilized feature maps and the smoothing of the loss
landscape. In addition, we provide a fundamental explanation for prior works -
namely, global average pooling, pre-activation, and ReLU6 - by addressing them
as special cases of spatial smoothing. These not only enhance accuracy, but
also improve uncertainty estimation and robustness by making the loss landscape
smoother in the same manner as spatial smoothing. The code is available at
https://github.com/xxxnell/spatial-smoothing.
