Robustness via Uncertainty-aware Cycle Consistency
2021
Unpaired image-to-image translation refers to learning inter-image-domain
mapping without corresponding image pairs. Existing methods learn deterministic
mappings without explicitly modelling the robustness to outliers or predictive
uncertainty, leading to performance degradation when encountering unseen
perturbations at test time. To address this, we propose a novel probabilistic
method based on Uncertainty-aware Generalized Adaptive Cycle Consistency
(UGAC), which models the per-pixel residual by generalized Gaussian
distribution, capable of modelling heavy-tailed distributions. We compare our
model with a wide variety of state-of-the-art methods on various challenging
tasks including unpaired image translation of natural images, using standard
datasets, spanning autonomous driving, maps, facades, and also in medical
imaging domain consisting of MRI. Experimental results demonstrate that our
method exhibits stronger robustness towards unseen perturbations in test data.
Code is released here:
https://github.com/ExplainableML/UncertaintyAwareCycleConsistency.
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