  Fast estimates of model uncertainty are required for many robust robotics
applications. Deep Ensembles provides state of the art uncertainty without
requiring Bayesian methods, but still it is computationally expensive. In this
paper we propose deep sub-ensembles, an approximation to deep ensembles where
the core idea is to ensemble only the layers close to the output, and not the
whole model. With ResNet-20 on the CIFAR10 dataset, we obtain 1.5-2.5 speedup
over a Deep Ensemble, with a small increase in error and NLL, and similarly up
to 5-15 speedup with a VGG-like network on the SVHN dataset. Our results show
that this idea enables a trade-off between error and uncertainty quality versus
computational performance.
