Self-training is an important class of unsupervised domain adaptation (UDA)
approaches that are used to mitigate the problem of domain shift, when applying
knowledge learned from a labeled source domain to unlabeled and heterogeneous
target domains. While self-training-based UDA has shown considerable promise on
discriminative tasks, including classification and segmentation, through
reliable pseudo-label filtering based on the maximum softmax probability, there
is a paucity of prior work on self-training-based UDA for generative tasks,
including image modality translation. To fill this gap, in this work, we seek
to develop a generative self-training (GST) framework for domain adaptive image
translation with continuous value prediction and regression objectives.
Specifically, we quantify both aleatoric and epistemic uncertainties within our
GST using variational Bayes learning to measure the reliability of synthesized
data. We also introduce a self-attention scheme that de-emphasizes the
background region to prevent it from dominating the training process. The
adaptation is then carried out by an alternating optimization scheme with
target domain supervision that focuses attention on the regions with reliable
pseudo-labels. We evaluated our framework on two cross-scanner/center,
inter-subject translation tasks, including tagged-to-cine magnetic resonance
(MR) image translation and T1-weighted MR-to-fractional anisotropy translation.
Extensive validations with unpaired target domain data showed that our GST
yielded superior synthesis performance in comparison to adversarial training
UDA methods.

本论文提出了一种基于自我训练的生成模型，通过连续值预测和回归目标函数进行领域自适应图像转换，其中使用变分 Bayes 学习量化整个生成过程中的不确定性并引入自我注意机制，最终在两个跨扫描仪 / 中心，跨受试者转换任务中，表现出了比当前经典的敌对训练 UDA 方法更优异的综合性能。

无监督域自适应医学图像翻译的关注连续生成自训练

Attentive Continuous Generative Self-training for Unsupervised Domain  Adaptive Medical Image Translation

Unsupervised domain adaptation (UDA) aims at inferring class labels for
unlabeled target domain given a related labeled source dataset. Intuitively, a
model trained on source domain normally produces higher uncertainties for
unseen data. In this work, we build on this assumption and propose to adapt
from source to target domain via calibrating their predictive uncertainties.
The uncertainty is quantified as the Renyi entropy, from which we propose a
general Renyi entropy regularization (RER) framework. We further employ
variational Bayes learning for reliable uncertainty estimation. In addition,
calibrating the sample variance of network parameters serves as a plug-in
regularizer for training. We discuss the theoretical properties of the proposed
method and demonstrate its effectiveness on three domain-adaptation tasks.

本文提出了一种基于源域推广到目标域的无监督领域适应方法，该方法通过校准源域和目标域的预测不确定性（以 Renyi 熵度量）来实现，使用变分贝叶斯学习可靠的不确定性估计，并讨论了其理论性质并在实验中验证了其有效性。