论文标题
内核相对 - 型光谱滤波，用于几次学习
Kernel Relative-prototype Spectral Filtering for Few-shot Learning
论文作者
论文摘要
很少有学习可以执行稀缺样本的分类任务和回归任务。作为最具代表性的少数学习模型之一，原型网络将每个类代表样品平均值或原型，并通过欧几里得距离来测量样品和原型的相似性。在本文中，我们提出了一个光谱滤波（收缩）框架，用于测量在繁殖的内核希尔伯特空间（RKHS）中，以测量查询样品和原型之间的差异，即相对原型。在此框架中，我们进一步提出了一种利用Tikhonov正则化作为几次分类的过滤功能的方法。我们进行了几项实验，以利用基于迷你imagenet数据集，层 - imagenet数据集和CIFAR-FS数据集的不同内核来验证我们的方法。实验结果表明，所提出的模型可以执行最新的模型。此外，实验结果表明，提出的收缩方法可以提高性能。源代码可从https://github.com/zhangtao2022/dsfn获得。
Few-shot learning performs classification tasks and regression tasks on scarce samples. As one of the most representative few-shot learning models, Prototypical Network represents each class as sample average, or a prototype, and measures the similarity of samples and prototypes by Euclidean distance. In this paper, we propose a framework of spectral filtering (shrinkage) for measuring the difference between query samples and prototypes, or namely the relative prototypes, in a reproducing kernel Hilbert space (RKHS). In this framework, we further propose a method utilizing Tikhonov regularization as the filter function for few-shot classification. We conduct several experiments to verify our method utilizing different kernels based on the miniImageNet dataset, tiered-ImageNet dataset and CIFAR-FS dataset. The experimental results show that the proposed model can perform the state-of-the-art. In addition, the experimental results show that the proposed shrinkage method can boost the performance. Source code is available at https://github.com/zhangtao2022/DSFN.
