A good feature representation is the key to image classification. In practice, image classifiers may be applied in scenarios different from what they have been trained on. This so-called domain shift leads to a significant performance drop in image classification. Unsupervised domain adaptation (UDA) reduces the domain shift by transferring the knowledge learned from a labeled source domain to an unlabeled target domain. We perform feature disentanglement for UDA by distilling category-relevant features and excluding category-irrelevant features from the global feature maps. This disentanglement prevents the network from overfitting to category-irrelevant information and makes it focus on information useful for classification. This reduces the difficulty of domain alignment and improves the classification accuracy on the target domain. We propose a coarse-to-fine domain adaptation method called Domain Adaptation via Feature Disentanglement~(DAFD), which has two components: (1)the Category-Relevant Feature Selection (CRFS) module, which disentangles the category-relevant features from the category-irrelevant features, and (2)the Dynamic Local Maximum Mean Discrepancy (DLMMD) module, which achieves fine-grained alignment by reducing the discrepancy within the category-relevant features from different domains. Combined with the CRFS, the DLMMD module can align the category-relevant features properly. We conduct comprehensive experiment on four standard datasets. Our results clearly demonstrate the robustness and effectiveness of our approach in domain adaptive image classification tasks and its competitiveness to the state of the art.

翻译：良好的特征表示是图像分类的关键。实际应用中，图像分类器可能面临与训练场景不同的测试环境，这种所谓的域偏移会导致图像分类性能显著下降。无监督域自适应（UDA）通过学习带标签源域知识并将其迁移至无标签目标域来减少域偏移。我们通过从全局特征图中提取类别相关特征并排除类别无关特征，实现面向UDA的特征解耦。这种解耦机制可防止网络对类别无关信息的过拟合，使其专注于对分类有效的特征，从而降低域对齐难度并提升目标域分类准确率。我们提出一种由粗到精的域自适应方法——基于特征解耦的域自适应（DAFD），该框架包含两个核心模块：（1）类别相关特征选择（CRFS）模块，实现类别相关特征与类别无关特征的解耦；（2）动态局部最大均值差异（DLMMD）模块，通过减少不同域间类别相关特征的差异性实现细粒度对齐。结合CRFS模块，DLMMD模块能够有效实现类别相关特征的精准对齐。我们在四个标准数据集上进行了全面实验，结果充分证明了该方法在域自适应图像分类任务中的鲁棒性和有效性，以及其与现有最优方法的竞争力。