摘要： 无监督域适应(Unsupervised Domain Adaptation, UDA) 旨在将知识从标记的源域迁移到未标记的目标域，从而提高目标域模型的性能。然而，传统的UDA方法假设源域和目标域的类别空间完全一致，无法处理目标域中存在的未知类别，这限制了其在实际场景中的应用。开放集域适应（Open-Set Domain Adaptation, OSDA）通过引入对未知类别的识别解决了这一问题，但如何有效减少域间差异和类别不平衡对模型性能的负面影响仍是一个重要挑战。现有的OSDA方法往往忽略了域特定特征，并简单地将域差异直接进行最小化。这可能导致类别之间的边界不清晰并削弱模型的泛化能力。因此，为了解决这一问题，本文提出通过最优传输距离正则化和近邻聚类方法的开放集域适应（Open-Set Domain Adaptation with Optimal Transport Distance Regularization and Neighborhood Clustering, OTRNC），该方法通过最优传输距离正则化方法来最大化高、低置信度样本组之间的分布距离，减少未知类别对域适应过程的干扰。之后利用动态近邻检索和不变特征学习，减少目标域内的类内变化，增强特征的泛化能力。实验结果表明，OTRNC在多个基准数据集上均表现出色。

Abstract: Unsupervised Domain Adaptation (UDA) aims to migrate knowledge from the labeled source domain to an unlabeled target domain to improve the performance of the target domain model. However, traditional UDA methods assume that the category spaces of the source domain and target domain are entirely consistent, making it impossible to handle unknown categories in the target domain. This limitation restricts their application in real-world scenarios. Open-Set Domain Adaptation (OSDA) addresses this issue by introducing recognition of unknown categories, but effectively reducing inter-domain differences and category imbalance remains a significant challenge. Existing OSDA methods often overlook domain specific features and simply minimize domain differences. This can lead to unclear boundaries between categories and weaken the model’s generalization ability. Therefore, to address this problem, this paper proposes Open-Set Domain Adaptation with Optimal Transport Distance Regularization and Neighborhood Clustering (OTRNC). This method maximizes the distribution distance between high and low confidence sample sets using optimal transport distance regularization, thereby reducing the interference of unknown categories in the domain adaptation process. Subsequently, dynamic nearest neighbor retrieval and invariant feature learning are employed to reduce intra-class variations within the target domain, enhancing feature generalization capabilities. Experimental results show that OTRNC performs well across multiple benchmark datasets.