Deep Sub-Ensembles for Fast Uncertainty Estimation in Image Classification

Understand

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.