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:
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本文提出了一种基于不确定性感知的广义自适应周期一致性的概率方法，利用广义高斯分布对像素残差进行建模，提高了对于测试数据的强鲁棒性，提高了不成对图像转换的性能。