Reliable quantification of epistemic and aleatoric uncertainty is of crucial
importance in applications where models are trained in one environment but
applied to multiple different environments, often seen in real-world
applications for example, in climate science or mobility analysis. We propose a
simple approach using surjective normalizing flows to identify
out-of-distribution data sets in deep neural network models that can be
computed in a single forward pass. The method builds on recent developments in
deep uncertainty quantification and generative modeling with normalizing flows.
We apply our method to a synthetic data set that has been simulated using a
mechanistic model from the mobility literature and several data sets simulated
from interventional distributions induced by soft and atomic interventions on
that model, and demonstrate that our method can reliably discern
out-of-distribution data from in-distribution data. We compare the surjective
flow model to a Dirichlet process mixture model and a bijective flow and find
that the surjections are a crucial component to reliably distinguish
in-distribution from out-of-distribution data.

通过使用满射规范化流方法，在深度神经网络模型中计算单次前向传播，可可靠地识别来自分布之外的数据集。该方法基于深度不确定性量化和正则化流中的最新进展，应用于合成数据集并与其他模型进行比较，表明满射流模型是可靠区分分布内外数据的关键组成部分。