Unsupervised image transfer enables intra- and inter-modality image
translation in applications where a large amount of paired training data is not
abundant. To ensure a structure-preserving mapping from the input to the target
domain, existing methods for unpaired image transfer are commonly based on
cycle-consistency, causing additional computational resources and instability
due to the learning of an inverse mapping. This paper presents a novel method
for uni-directional domain mapping that does not rely on any paired training
data. A proper transfer is achieved by using a GAN architecture and a novel
generator loss based on patch invariance. To be more specific, the generator
outputs are evaluated and compared at different scales, also leading to an
increased focus on high-frequency details as well as an implicit data
augmentation. This novel patch loss also offers the possibility to accurately
predict aleatoric uncertainty by modeling an input-dependent scale map for the
patch residuals. The proposed method is comprehensively evaluated on three
well-established medical databases. As compared to four state-of-the-art
methods, we observe significantly higher accuracy on these datasets, indicating
great potential of the proposed method for unpaired image transfer with
uncertainty taken into account. Implementation of the proposed framework is
released here:
https://github.com/anger-man/unsupervised-image-transfer-and-uq.

本文提出了一种不需要配对训练数据的单向域映射方法，使用 GAN 架构和基于补丁不变性的新型生成器损失来实现适当的图像转换，并通过皮片残差建立了一个输入相关的比例图来准确预测不确定性。通过在三个医学数据集上全面评估，相较于四种最先进方法，我们观察到更高的准确性，表明该方法用于无教师图像转换具有很大的潜力。