By using unsupervised domain adaptation (UDA), knowledge can be transferred from a label-rich source domain to a target domain that contains relevant information but lacks labels. Many existing UDA algorithms suffer from directly using raw images as input, resulting in models that overly focus on redundant information and exhibit poor generalization capability. To address this issue, we attempt to improve the performance of unsupervised domain adaptation by employing the Fourier method (FTF).Specifically, FTF is inspired by the amplitude of Fourier spectra, which primarily preserves low-level statistical information. In FTF, we effectively incorporate low-level information from the target domain into the source domain by fusing the amplitudes of both domains in the Fourier domain. Additionally, we observe that extracting features from batches of images can eliminate redundant information while retaining class-specific features relevant to the task. Building upon this observation, we apply the Fourier Transform at the data stream level for the first time. To further align multiple sources of data, we introduce the concept of correlation alignment. To evaluate the effectiveness of our FTF method, we conducted evaluations on four benchmark datasets for domain adaptation, including Office-31, Office-Home, ImageCLEF-DA, and Office-Caltech. Our results demonstrate superior performance.

翻译：通过无监督领域自适应（UDA），可以从标签丰富的源域向包含相关信息但缺少标签的目标域迁移知识。现有许多UDA算法直接使用原始图像作为输入，导致模型过度关注冗余信息且泛化能力较差。为解决该问题，我们尝试采用傅里叶方法（FTF）提升无监督领域自适应的性能。具体而言，FTF的灵感来源于傅里叶频谱的幅值，该幅值主要保留低层统计信息。在FTF中，我们通过将源域和目标域在傅里叶域中的幅值进行融合，从而有效将目标域的低层信息融入源域。此外，我们观察到从批量图像中提取特征可消除冗余信息，同时保留与任务相关的类别特定特征。基于此观察，我们首次在数据流层面应用傅里叶变换。为进一步对齐多源数据，我们引入了相关性对齐的概念。为评估FTF方法的有效性，我们在Office-31、Office-Home、ImageCLEF-DA和Office-Caltech四个领域自适应基准数据集上进行了评估。实验结果表明该方法具有优越性能。