  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
https://github.com/Hehxcf/CPC/.
