In this paper, we address unsupervised domain adaptation under noisy
environments, which is more challenging and practical than traditional domain
adaptation. In this scenario, the model is prone to overfitting noisy labels,
resulting in a more pronounced domain shift and a notable decline in the
overall model performance. Previous methods employed prototype methods for
domain adaptation on robust feature spaces. However, these approaches struggle
to effectively classify classes with similar features under noisy environments.
To address this issue, we propose a new method to detect and correct confusing
class pair. We first divide classes into easy and hard classes based on the
small loss criterion. We then leverage the top-2 predictions for each sample
after aligning the source and target domain to find the confusing pair in the
hard classes. We apply label correction to the noisy samples within the
confusing pair. With the proposed label correction method, we can train our
model with more accurate labels. Extensive experiments confirm the
effectiveness of our method and demonstrate its favorable performance compared
with existing state-of-the-art methods. Our codes are publicly available at
this https URL

我们提出了一种处理嘈杂环境下无监督领域适应问题的方法，通过检测和修正混淆的类对，利用准确的标签来训练模型，并在实验中验证了我们方法的有效性和性能。

基于类别原型的混淆对纠正方法用于噪声环境下的领域适应

Confusing Pair Correction Based on Category Prototype for Domain  Adaptation under Noisy Environments

Noisy labels can significantly affect the performance of deep neural networks
(DNNs). In medical image segmentation tasks, annotations are error-prone due to
the high demand in annotation time and in the annotators' expertise. Existing
methods mostly assume noisy labels in different pixels are \textit{i.i.d}.
However, segmentation label noise usually has strong spatial correlation and
has prominent bias in distribution. In this paper, we propose a novel Markov
model for segmentation noisy annotations that encodes both spatial correlation
and bias. Further, to mitigate such label noise, we propose a label correction
method to recover true label progressively. We provide theoretical guarantees
of the correctness of the proposed method. Experiments show that our approach
outperforms current state-of-the-art methods on both synthetic and real-world
noisy annotations.

医学图像分割中标签噪声对深度神经网络（DNN）的性能产生显著影响。本文提出了一种新颖的马尔可夫模型用于分割带有噪声标注的图像，并通过标签校正方法逐步恢复真实标签。实验证明，我们的方法在合成和真实世界的噪声标注数据上优于当前最先进的方法。