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 the model from few samples, GNN
usually suffers from severe over-fitting and over-smoothing as the model
becomes deep, which limit the scalability. In this work, we propose a novel
Attentive GNN to tackle these challenges, by incorporating a triple-attention
mechanism, i.e. 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 model achieves the promising
results, comparing to the state-of-the-art GNN- and CNN-based methods for
few-shot learning tasks, over the mini-ImageNet and tiered-ImageNet benchmarks,
under ConvNet-4 and ResNet-based backbone with both inductive and transductive
settings. The codes will be made publicly available.

本研究提出了一种新型的注意力图神经网络 (Attentive GNN)，该网络通过三重注意力机制（节点自注意力、邻域注意力、层记忆注意力）处理 GNN 深度学习中存在的过拟合和过度平滑问题，从而最终提升了其在小样本学习任务上的性能表现。实验证明，相比于其他现有的 GNN 和 CNN 方法，在深度神经网络骨架 (ConvNet-4 和 ResNet) 和归纳、传导方式下，在 mini-ImageNet 和 tiered-ImageNet 数据集上，该新型图神经网络取得了令人满意的结果。