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。

CNA-TTA：在聚类中基于干净和噪声区域的特征学习用于在线离线测试时间自适应

CNA-TTA: Clean and Noisy Region Aware Feature Learning within Clusters  for Online-Offline Test-Time Adaptation

We propose a novel framework to perform classification via deep learning in
the presence of noisy annotations. When trained on noisy labels, deep neural
networks have been observed to first fit the training data with clean labels
during an "early learning" phase, before eventually memorizing the examples
with false labels. We prove that early learning and memorization are
fundamental phenomena in high-dimensional classification tasks, even in simple
linear models, and give a theoretical explanation in this setting. Motivated by
these findings, we develop a new technique for noisy classification tasks,
which exploits the progress of the early learning phase. In contrast with
existing approaches, which use the model output during early learning to detect
the examples with clean labels, and either ignore or attempt to correct the
false labels, we take a different route and instead capitalize on early
learning via regularization. There are two key elements to our approach. First,
we leverage semi-supervised learning techniques to produce target probabilities
based on the model outputs. Second, we design a regularization term that steers
the model towards these targets, implicitly preventing memorization of the
false labels. The resulting framework is shown to provide robustness to noisy
annotations on several standard benchmarks and real-world datasets, where it
achieves results comparable to the state of the art.

提出了一种基于早期学习的新型噪声分类技术框架，使用半监督学习的目标概率和正则化项，防止深层神经网络过于依赖错误标注而导致的过拟合现象。测试结果表明，该方法在多个标准基准数据集和实际数据集上均能达到与现有先进技术可比的鲁棒性。