  The ability to estimate epistemic uncertainty is often crucial when deploying
machine learning in the real world, but modern methods often produce
overconfident, uncalibrated uncertainty predictions. A common approach to
quantify epistemic uncertainty, usable across a wide class of prediction
models, is to train a model ensemble. In a naive implementation, the ensemble
approach has high computational cost and high memory demand. This challenges in
particular modern deep learning, where even a single deep network is already
demanding in terms of compute and memory, and has given rise to a number of
attempts to emulate the model ensemble without actually instantiating separate
ensemble members. We introduce FiLM-Ensemble, a deep, implicit ensemble method
based on the concept of Feature-wise Linear Modulation (FiLM). That technique
was originally developed for multi-task learning, with the aim of decoupling
different tasks. We show that the idea can be extended to uncertainty
quantification: by modulating the network activations of a single deep network
with FiLM, one obtains a model ensemble with high diversity, and consequently
well-calibrated estimates of epistemic uncertainty, with low computational
overhead in comparison. Empirically, FiLM-Ensemble outperforms other implicit
ensemble methods, and it and comes very close to the upper bound of an explicit
ensemble of networks (sometimes even beating it), at a fraction of the memory
cost.
