Edge-labeling based modified gated graph network for few-shot learning

Accurate determination of similarity between samples is fundamental and critical for graph network based few-shot learning tasks. Previous approaches typically employ convolutional neural networks to obtain relations between nodes. However, these networks are not adept at handling node features in vector form. To overcome this limitation, we proposed a modified gated graph network (MGGN) that uniquely integrates graph networks and modified gated recurrent units (M-GRU) for few-shot classification. The introduced M-GRU mitigates the loss of label information from the initial graph and reduces computational complexity. The MGGN contains two modules that alternately update node and edge features. The node update module leverages a gating mechanism to integrate edge features into node update weights, fostering a learnable node aggregation process. The edge update component perceives the trend in edge feature changes and establishes long-term dependencies. Experimental results on two benchmark datasets demonstrate that our MGGN achieves comparable performance to state-of-the-art methods. The code is available at https://github.com/zpx16900/MGGN.