Robustnessvia Uncertainty-awareCycleConsistency

Existing methods learn deterministic mappings without explicitly modelling the robustness to outliers or predictive uncertainty,leading to performance degradation when encountering unseen perturbations attest time. Toaddress this, we propose anovelprobabilistic method based on Uncertainty-aware Generalized AdaptiveCycle Consistency(UGAC), which models the per-pixel residual by generalized Gaussian distribution, capable of modelling heavy-tailed distributions.