域适应旨在利用源域中的标注信息帮助目标域任务的进行. 逐域和逐类的对齐通常是域适应的先决条件. 然而, 上述过程没有充分利用样本级别的信息, 导致对齐结果不理想. 基于几何视角充分利用样本信息, 获得了更精细的保持结构的对齐. 首先, 在分布适应的基础上使用光滑三元组损失, 得到基于样本层面的对齐; 然后, 将结构正则项引入域适应中, 保持数据的几何结构, 并利用内蕴最速下降法对优化问题进行求解, 保证解空间的结构; 最后, 在自然图像数据集和医学数据集上, 将本方法与度量学习和域适应算法进行比较. 结果证明了所提出算法的有效性.

Domain adaptation methods aim to assist tasks in a given target domain by using labeling information from a source domain. Domain-wise and class-wise alignments are commonly used as prerequisites for domain adaptation. However, sample-wise information is not fully utilized, which leads to unsatisfactory alignment results. In this study, we fully use sample-wise information from a geometric perspective to obtain a finer structure-preserving alignment. First, we use the smooth triplet loss to obtain a sample-wise alignment based on distribution adaptation. Then, we introduce a structure regularization term to perform domain adaptation to maintain the geometric structure of data, and use the intrinsic steepest descent algorithm to solve the optimization problem to ensure the structure of the solution space. Finally, we compare our approach with state-of-the-art metric learning and domain adaptation algorithms on datasets comprising natural images and medical information. Experimental results are presented to demonstrate the effectiveness of our proposed algorithm.