  Unpaired image-to-image translation refers to learning inter-image-domain
mapping in an unsupervised manner. Existing methods often learn deterministic
mappings without explicitly modelling the robustness to outliers or predictive
uncertainty, leading to performance degradation when encountering unseen
out-of-distribution (OOD) patterns at test time. To address this limitation, we
propose a novel probabilistic method called 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 two challenging tasks: unpaired image denoising in the natural image
and unpaired modality prorogation in medical image domains. Experimental
results demonstrate that our model offers superior image generation quality
compared to recent methods in terms of quantitative metrics such as
signal-to-noise ratio and structural similarity. Our model also exhibits
stronger robustness towards OOD test data.
