Universal Domain Alignment Framework for Classification and Regression Tasks

Unsupervised Domain Adaptation (UDA), a critical technique for transferring knowledge from labeled source domains to unlabeled target domains, holds significant value in both classification and regression tasks. However, existing methods are often designed for a single task, lacking a unified framework that can handle both types of tasks simultaneously. Moreover, while global distribution alignment can reduce the overall discrepancy between the source and target domains, inconsistencies in local feature spaces persist. These local inconsistencies may introduce biases in high-level semantic structures, increasing the variability of feature distributions and significantly weakening the model’s generalization ability in the target domain. To address these challenges, we propose a Universal Domain Alignment Framework (UDAF), which achieves unified modeling for classification and regression tasks while reducing local distribution discrepancies through the Target Space Coupling Module (TSCM) and the Domain Embedding Consistency Loss (DECL). In UDAF, TSCM optimizes the structure of the output space to meet the requirements of both classification and regression tasks. Specifically, for labeled source domain data, we minimize the distance between features and their corresponding class centers to cluster similar samples in the output space. For unlabeled target domain data, pseudo labels are generated using prediction probabilities to estimate target class centers, and the class centers are dynamically updated to optimize the output space structure. Meanwhile, DECL models local feature alignment from a sample-to-domain perspective. By measuring the distance between target domain samples and the orthogonal basis of source domain subspaces, DECL explicitly captures local distribution biases and incorporates them as optimization objectives, further enhancing the fine-grained alignment of cross-domain features. The proposed UDAF is highly extensible and can be seamlessly integrated into existing methods. Comprehensive experiments conducted on seven datasets demonstrate that this approach achieves a performance improvement, especially with an 2.1% improvement on DomainNet dataset.