Neural Bootstrapper

Bootstrapping has been a primary tool for uncertainty quantification, and their theoretical and computational properties have been investigated in the field of statistics and machine learning. However, due to its nature of repetitive computations, the computational burden required to implement bootstrap procedures for the neural network is painfully heavy, and this fact seriously hurdles the practical use of these procedures on the uncertainty estimation of modern deep learning. To overcome the inconvenience, we propose a procedure called \emph{Neural Bootstrapper} (NeuBoots). We reveal that the NeuBoots stably generate valid bootstrap samples that coincide with the desired target samples with minimal extra computational cost compared to traditional bootstrapping. Consequently, NeuBoots makes it feasible to construct bootstrap confidence intervals of outputs of neural networks and quantify their predictive uncertainty. We also suggest NeuBoots for deep convolutional neural networks to consider its utility in image classification tasks, including calibration, detection of out-of-distribution samples, and active learning. Empirical results demonstrate that NeuBoots is significantly beneficial for the above purposes.