Title: Self-Distribution Distillation: Efficient Uncertainty Estimation. Abstract: Deep learning is increasingly being applied in safety-critical domains. For these scenarios it is important to know the level of uncertainty in a model’s prediction to ensure that appropriate decisions are made by a system. Deep ensembles are the de-facto standard approach to obtaining various measures of uncertainty. However, ensembles normally significantly increase the resources required in both the training and deployment phases. Approaches have been developed that typically address the costs in one of these phases. In this work we propose a novel training approach, self-distribution distillation (S2D), which is able to efficiently, both in time and memory, train a single model that can estimate uncertainties in an integrated training phase. Furthermore it is possible to build ensembles of these models and apply ensemble distillation approaches, hierarchical distribution distillation, in cases where one is less limited by computational resources in the training phase, but still requires efficiency in the deployment phase. Experiments on CIFAR-100 showed that S2D models outperformed standard models and Monte-Carlo dropout. Additional out-of-distribution detection experiments on LSUN, Tiny ImageNet, SVHN showed that even a standard deep ensemble can be outperformed using S2D based ensembles and novel distilled models.
