Kernel Relative-prototype Spectral Filtering for Few-shot Learning
This work addresses the problem of classification with scarce samples for machine learning applications, representing an incremental improvement over existing prototype-based methods.
The paper tackles few-shot learning by proposing a spectral filtering framework in a reproducing kernel Hilbert space to measure differences between query samples and prototypes, achieving state-of-the-art performance on datasets like miniImageNet, tiered-ImageNet, and CIFAR-FS, with the shrinkage method boosting results.
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.