Few-Shot Learning With Dynamic Graph Structure Preserving

In recent years, few-shot learning has received increasing attention in the Internet of Things areas. Few-shot learning aims to distinguish unseen classes with a few labeled samples from each class. Most recently transductive few-shot studies highly rely on the static geometry distributions generated on the feature space during the label propagation process between unseen class instances. However, these recent methods fail to guarantee that the generated graph structure preserves the true distributions between data properly. In this article, we propose a novel dynamic graph structure preserving (DGSP) model for few-shot learning. Specifically, we formulate the objective function of DGSP by simultaneously considering the data correlations from the feature space and the label space to update the generated graph structure, which can reasonably revise the inappropriate or mistaken local geometry relationships. Then, we design an efficient alternating optimization algorithm to jointly learn the label prediction matrix and the optimal graph structure, the latter of which can be formulated as a linear programming problem. Moreover, our proposed DGSP can be easily combined with any backbone networks during the learning process. We conduct extensive experimental results across different benchmarks, backbones, and task settings, and our method achieves state-of-the-art performance compared with methods based on transductive few-shot learning.