论文标题
跨域通过源造型的几个分类分类
Cross-Domain Few-Shot Classification via Inter-Source Stylization
论文作者
论文摘要
跨域几乎没有射击分类（CDFSC）的目的是通过利用有限标记的辅助数据集的知识来准确地对目标数据集进行有限的标记数据进行分类，尽管两个数据集的域之间存在差异。一些现有方法需要来自多个域的样品进行模型培训。但是，当样本标签稀缺时，这些方法失败了。为了克服这一挑战，本文提出了一种利用多个源域的解决方案，而无需额外的标签成本。具体而言，一个源域之一是完全标记的，而其他源域则没有标记。然后引入源中源样式网络（ISSNET），以增强跨多个源域，丰富数据分布和模型的泛化功能的风格。 8个目标数据集的实验表明，与多种基线方法相比，来自多个源数据的ISSNET利用未标记的数据显着降低了域间隙对分类性能的负面影响。
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.
