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。

基于傅里叶变换的领域自适应框架

A Fourier Transform Framework for Domain Adaptation

Domain adaptation (DA) and domain generalization (DG) have emerged as a
solution to the domain shift problem where the distribution of the source and
target data is different. The task of DG is more challenging than DA as the
target data is totally unseen during the training phase in DG scenarios. The
current state-of-the-art employs adversarial techniques, however, these are
rarely considered for the DG problem. Furthermore, these approaches do not
consider correlation alignment which has been proven highly beneficial for
minimizing domain discrepancy. In this paper, we propose a correlation-aware
adversarial DA and DG framework where the features of the source and target
data are minimized using correlation alignment along with adversarial learning.
Incorporating the correlation alignment module along with adversarial learning
helps to achieve a more domain agnostic model due to the improved ability to
reduce domain discrepancy with unlabeled target data more effectively.
Experiments on benchmark datasets serve as evidence that our proposed method
yields improved state-of-the-art performance.

本文提出了一种基于相关性感知的对抗式 DA 和 DG 框架，将源数据和目标数据的特征最小化，通过关联对齐模块和对抗学习来实现更加领域无关的模型，并在基准数据集上进行的实验证明了我们提出的方法的最新效果得到了改善。

Correlation-aware Adversarial Domain Adaptation and Generalization

In this paper, we propose to learn shared semantic space with correlation
alignment (${S}^{3}CA$) for multimodal data representations, which aligns
nonlinear correlations of multimodal data distributions in deep neural networks
designed for heterogeneous data. In the context of cross-modal (event)
retrieval, we design a neural network with convolutional layers and
fully-connected layers to extract features for images, including images on
Flickr-like social media. Simultaneously, we exploit a fully-connected neural
network to extract semantic features for texts, including news articles from
news media. In particular, nonlinear correlations of layer activations in the
two neural networks are aligned with correlation alignment during the joint
training of the networks. Furthermore, we project the multimodal data into a
shared semantic space for cross-modal (event) retrieval, where the distances
between heterogeneous data samples can be measured directly. In addition, we
contribute a Wiki-Flickr Event dataset, where the multimodal data samples are
not describing each other in pairs like the existing paired datasets, but all
of them are describing semantic events. Extensive experiments conducted on both
paired and unpaired datasets manifest the effectiveness of ${S}^{3}CA$,
outperforming the state-of-the-art methods.

该研究论文介绍了使用 $S^{3} CA$ 学习多模态数据表示的共享语义空间，利用多层卷积网络和全连接网络对图片和文本提取特征，从而实现跨模态（事件）检索，并将多模态数据投影到共享语义空间中进行检索。同时，作者贡献了一个 Wiki-Flickr 事件数据集，其中的多模态数据样本不像现有的配对数据集那样是成对描述的，而是都描述了语义事件。实验结果表明，$S^{3} CA$ 有效，优于当前最先进的方法。