论文标题
细心的图形神经网络，用于几次学习
Attentive Graph Neural Networks for Few-Shot Learning
论文作者
论文摘要
图形神经网络（GNN）已经证明了许多具有挑战性的应用程序（包括少数图的学习任务）的表现出色。尽管它可以从几个样本中学习和概括模型的能力强大，但随着模型变得深度，GNN通常会遭受严重的过度拟合和过度平滑的影响，从而限制了可伸缩性。在这项工作中，我们提出了一种新颖的专注GNN来解决这些挑战，通过结合三重注意机制，即节点自我注意力，邻里注意力和记忆注意力。我们解释了为什么提出的细心模块可以通过理论分析和插图来改善几次学习的GNN。广泛的实验表明，在Convnet-4和基于Resnet的基本背景下，与Mini-Imimagenet和Siered-Imagenet基准测试相比，与最先进的GNN和CNN基于少量学习任务的方法相比，与最先进的GNN和CNN的方法相比，实现了有希望的结果。这些代码将公开可用。
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.
