A domain shift occurs when training (source) and test (target) data diverge
in their distribution. Test-time adaptation (TTA) addresses the domain shift
problem, aiming to adopt a trained model on the source domain to the target
domain in a scenario where only a well-trained source model and unlabeled
target data are available. In this scenario, handling false labels in the
target domain is crucial because they negatively impact the model performance.
To deal with this problem, we propose to utilize cluster structure (i.e.,
{`Clean'} and {`Noisy'} regions within each cluster) in the target domain
formulated by the source model. Given an initial clustering of target samples,
we first partition clusters into {`Clean'} and {`Noisy'} regions defined based
on cluster prototype (i.e., centroid of each cluster). As these regions have
totally different distributions of the true pseudo-labels, we adopt distinct
training strategies for the clean and noisy regions: we selectively train the
target with clean pseudo-labels in the clean region, whereas we introduce mixup
inputs representing intermediate features between clean and noisy regions to
increase the compactness of the cluster. We conducted extensive experiments on
multiple datasets in online/offline TTA settings, whose results demonstrate
that our method, {CNA-TTA}, achieves state-of-the-art for most cases.

利用聚类结构和区域的方法进行测试时间自适应，从而解决域偏移和虚假标签问题，该方法在在线 / 离线自适应模式和多个数据集上达到了最先进的效果。