Attentive Graph Neural Networks for Few-Shot Learning

• URL: http://arxiv.org/abs/2007.06878v2
• Date: Fri, 2 Oct 2020 13:08:56 GMT
• Title: Attentive Graph Neural Networks for Few-Shot Learning
• Authors: Hao Cheng, Joey Tianyi Zhou, Wee Peng Tay and Bihan Wen
• Abstract summary: 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. We propose a novel Attentive GNN to tackle these challenges, by incorporating a triple-attention mechanism.
• Score: 74.01069516079379
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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