Attentive Graph Neural Networks for Few Shot Learning
Graph Neural Networks (GNN) has demonstrated the superior performance in many challenging applications, including the few shot learning tasks. Despite its powerful capacity to learn and generalize from few samples, GNN usually suffers from severe over fitting and over smoothing as the model becomes deep, which limit the model scalability. In this work, we propose a novel Attentive GNN to tackle these challenges, by incorporating a triple attention mechanism, \ie node self attention, neighborhood attention, and layer memory attention. We explain why the proposed attentive modules can improve GNN for few shot learning with theoretical analysis and illustrations. Extensive experiments show that the proposed Attentive GNN outperforms the state of the art GNN based methods for few shot learning over the mini ImageNet and Tiered ImageNet datasets, with both inductive and transductive settings.
ArXiv