Mixture Distribution Graph Network for Few Shot Learning

Abstract

Few-shot learning aims at heuristically resolving new tasks with limited labeled data; most of the existing approaches are affected by knowledge learned from similar experiences. However, interclass barriers and new samples insufficiency limit the transfer of knowledge. In this article, we propose a novel mixture distribution graph network, in which the interclass relation is explicitly modeled and propagated via graph generation. Owing to the weighted distribution features based on the Gaussian mixture model, we take class diversity into consideration, thereby utilizing information precisely and efficiently. Equipped with minimal gated units, the “memory” of similar tasks can be preserved and reused through episode training, which fills a gap in temporal characteristics and softens the impact of data insufficiency. Extensive trials are carried out based on the MiniImageNet and CIFAR-FS data sets. The results turn out that our method exceeds most state-of-the-art approaches, which shows the validity and universality of our method in few-shot learning.