Uncertainty-aware Generalized Adaptive CycleGAN
This work addresses robustness issues in unpaired image translation for applications like natural image denoising and medical image modality propagation, though it appears incremental as it builds on existing CycleGAN frameworks.
The paper tackles the problem of unpaired image-to-image translation by addressing performance degradation with unseen out-of-distribution patterns, proposing a probabilistic method that models per-pixel residuals with generalized Gaussian distribution. The result shows superior image generation quality in terms of signal-to-noise ratio and structural similarity, along with stronger robustness to OOD test data.
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.