Abstract
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.
Abstract (translated)
好的特征表示是图像分类的关键。实际上,图像分类器可能应用于与它们所训练的场景不同的场景。这种所谓的跨域导致图像分类性能显著下降。无监督跨域适应(UDA)通过将从标记源域学到的知识转移到未标记目标域来减少跨域。我们进行特征分离以进行UDA,通过归纳类别相关的特征并排除类别无关的特征从全局特征映射中删除。这种分离可以防止网络过度拟合无关信息并使其专注于分类有用的信息。这减少了跨域匹配的难度并提高了目标域的分类精度。我们提出了一种粗到精的跨域适应方法,称为“跨域适应通过特征分离~(DAFD)”,它有两个组件:(1)类别相关的特征选择(CRFS)模块,它将类别相关的特征从类别无关的特征中分离出来,(2)动态局部最大平均差异(DLMMD)模块,它通过减少不同域中类别相关的特征之间的差异来实现精细匹配。将CRFS与DLMMD模块组合在一起,DLMMD模块可以正确地对齐类别相关的特征。我们进行了四个标准数据集的全面实验。我们的结果表明,我们在跨域适应图像分类任务中的方法的稳健性和有效性,以及它与当前技术的竞争的水平。
URL
https://arxiv.org/abs/2301.13337
