Domain adaptation typically requires to access source domain data to utilize
their distribution information for domain alignment with the target data.
However, in many real-world scenarios, the source data may not be accessible
during the model adaptation in the target domain due to privacy issue. This
paper studies the practical yet challenging source-free unsupervised domain
adaptation problem, in which only an existing source model and the unlabeled
target data are available for model adaptation. We present a novel denoised
pseudo-labeling method for this problem, which effectively makes use of the
source model and unlabeled target data to promote model self-adaptation from
pseudo labels. Importantly, considering that the pseudo labels generated from
source model are inevitably noisy due to domain shift, we further introduce two
complementary pixel-level and class-level denoising schemes with uncertainty
estimation and prototype estimation to reduce noisy pseudo labels and select
reliable ones to enhance the pseudo-labeling efficacy. Experimental results on
cross-domain fundus image segmentation show that without using any source
images or altering source training, our approach achieves comparable or even
higher performance than state-of-the-art source-dependent unsupervised domain
adaptation methods.

本文研究实际且具有挑战性的无源无监督域自适应问题。作者提出了一种新颖的去噪假标记方法，该方法利用源模型和未标记的目标数据促进模型的自适应。该方法通过不确定性估计和原型估计，进一步引入了两种补充的像素级和类级去噪方案，以减少嘈杂的假标记并选择可靠的假标记以增强假标记的有效性。实验表明，该方法在不使用任何源图像或修改源训练的情况下，实现了与最先进的源相关无监督领域适应方法可比甚至更高的性能。