Uncertainty quantification and out-of-distribution detection using surjective normalizing flows
This addresses the challenge of model reliability when applied to multiple environments, such as in climate science or mobility analysis, though it appears incremental by building on existing uncertainty quantification and normalizing flow techniques.
The authors tackled the problem of uncertainty quantification and out-of-distribution detection in deep neural networks, proposing a method using surjective normalizing flows that reliably distinguishes out-of-distribution from in-distribution data in synthetic datasets.
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.