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.

使用傅里叶方法（FTF）改善无监督领域适应性，通过融合傅里叶域中源域和目标域的振幅，将目标域的低级信息有效地整合到源域中，并引入相关性对齐的概念进一步调整多源数据，以实现卓越的性能。