Enabling Uncertainty Estimation in
Iterative Neural Networks

1Computer Vision Laboratory, EPFL 2Neural Concept SA
ICML 2024
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Teaser Image

Uncertainty in recursive models. Such models use their initial predictions as inputs to produce subsequent predictions. We display the output of three consecutive iterations of a network trained to compute distance maps to road pixels. (Top:) All roads are clearly visible. The three maps are similar and the per pixel variance is low. (Bottom:) The road in the red square is tree-covered. It is eventually detected properly but the variance is high.


Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of their successive outputs is highly correlated with the accuracy of the value to which they converge. Thus, we can use the convergence rate as a useful proxy for uncertainty. This results in an approach to uncertainty estimation that provides state-of-the-art estimates at a much lower computational cost than techniques like Ensembles, and without requiring any modifications to the original iterative model. We demonstrate its practical value by embedding it in two application domains: road detection in aerial images and the estimation of aerodynamic properties of 2D and 3D shapes.