Unsupervised domain adaptation (UDA) is a well-explored domain in transfer learning, finding applications across various real-world scenarios. The central challenge in UDA lies in addressing the domain shift between training (source) and testing (target) data distributions. This study focuses on image classification tasks within UDA, where label spaces are shared, but the target domain lacks labeled samples. Our primary objective revolves around mitigating the domain discrepancies between the source and target domains, ultimately facilitating robust generalization in the target domains. Domain adaptation techniques have traditionally concentrated on the global feature distribution to minimize disparities. However, these methods often need to pay more attention to crucial, domain-specific subdomain information within identical classification categories, challenging achieving the desired performance without fine-grained data. To tackle these challenges, we propose a unified framework, Subdomain Adaptation via Correlation Alignment with Entropy Minimization, for unsupervised domain adaptation. Our approach incorporates three advanced techniques: (1) Local Maximum Mean Discrepancy, which aligns the means of local feature subsets, capturing intrinsic subdomain alignments often missed by global alignment, (2) correlation alignment aimed at minimizing the correlation between domain distributions, and (3) entropy regularization applied to target domains to encourage low-density separation between categories. We validate our proposed methods through rigorous experimental evaluations and ablation studies on standard benchmark datasets. The results consistently demonstrate the superior performance of our approaches compared to existing state-of-the-art domain adaptation methods.

无监督域适应 (UDA) 是迁移学习中一个经过充分探索的领域，可在各种现实场景中找到应用。UDA 的核心挑战在于解决训练（源）和测试（目标）数据分布之间的领域转换。本研究重点关注 UDA 中的图像分类任务，其中标签空间是共享的，但目标域缺乏标记样本。我们的主要目标围绕减轻源域和目标域之间的域差异，最终促进目标域的稳健泛化。领域适应技术传统上集中在全局特征分布上，以尽量减少差异。然而，这些方法通常需要更多地关注相同分类类别中关键的、特定领域的子域信息，在没有细粒度数据的情况下实现所需的性能具有挑战性。为了应对这些挑战，我们提出了一个统一的框架，即通过相关对齐和熵最小化进行子域适应，用于无监督域适应。我们的方法采用了三种先进技术：（1）局部最大平均差异，它对齐局部特征子集的平均值，捕获全局对齐经常错过的内在子域对齐，（2）相关对齐旨在最小化域分布之间的相关性，以及（ 3）应用于目标域的熵正则化以鼓励类别之间的低密度分离。我们通过对标准基准数据集进行严格的实验评估和消融研究来验证我们提出的方法。结果始终证明，与现有最先进的域适应方法相比，我们的方法具有卓越的性能。