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Deep Deterministic Uncertainty for Semantic Segmentation
[article]
2021
arXiv
pre-print
Preserved Fulltext
We extend Deep Deterministic Uncertainty (DDU), a method for uncertainty estimation using feature space densities, to semantic segmentation. DDU enables quantifying and disentangling epistemic and aleatoric uncertainty in a single forward pass through the model. We study the similarity of feature representations of pixels at different locations for the same class and conclude that it is feasible to apply DDU location independently, which leads to a significant reduction in memory consumption
arXiv:2111.00079v1
fatcat:xal74vuiwfdongiio2d3dy2p3e