The goal of Cross-Domain Few-Shot Classification (CDFSC) is to accurately classify a target dataset with limited labelled data by exploiting the knowledge of a richly labelled auxiliary dataset, despite the differences between the domains of the two datasets. Some existing approaches require labelled samples from multiple domains for model training. However, these methods fail when the sample labels are scarce. To overcome this challenge, this paper proposes a solution that makes use of multiple source domains without the need for additional labeling costs. Specifically, one of the source domains is completely tagged, while the others are untagged. An Inter-Source Stylization Network (ISSNet) is then introduced to enhance stylisation across multiple source domains, enriching data distribution and model's generalization capabilities. Experiments on 8 target datasets show that ISSNet leverages unlabelled data from multiple source data and significantly reduces the negative impact of domain gaps on classification performance compared to several baseline methods.

翻译：跨域少样本分类（CDFSC）的目标是利用标注丰富的辅助数据集的知识，对标签样本有限的目标数据集进行精确分类，尽管两个数据集的域存在差异。现有的一些方法需要来自多个域的标注样本进行模型训练。然而，当样本标签稀缺时，这些方法会失效。为克服这一挑战，本文提出了一种利用多个源域且无需额外标注成本的解决方案。具体而言，其中一个源域被完全标注，而其他源域未被标注。随后引入了一种源间风格化网络（ISSNet），以增强多个源域之间的风格化，丰富数据分布并提升模型的泛化能力。在8个目标数据集上的实验表明，与多种基线方法相比，ISSNet利用了来自多个源域的未标注数据，显著降低了域差异对分类性能的负面影响。