Edge-Labeling based Directed Gated Graph Network for Few-shot Learning
This work addresses a domain-specific problem in few-shot learning by improving similarity computation in graph networks, but it is incremental as it builds on existing graph-network methods.
The paper tackles the mismatch between CNNs and vector-form node features in graph-network-based few-shot learning by proposing an edge-labeling-based directed gated graph network (DGGN) that uses gated recurrent units to update node similarities, achieving comparable performance to state-of-the-art methods on two benchmark datasets.
Existing graph-network-based few-shot learning methods obtain similarity between nodes through a convolution neural network (CNN). However, the CNN is designed for image data with spatial information rather than vector form node feature. In this paper, we proposed an edge-labeling-based directed gated graph network (DGGN) for few-shot learning, which utilizes gated recurrent units to implicitly update the similarity between nodes. DGGN is composed of a gated node aggregation module and an improved gated recurrent unit (GRU) based edge update module. Specifically, the node update module adopts a gate mechanism using activation of edge feature, making a learnable node aggregation process. Besides, improved GRU cells are employed in the edge update procedure to compute the similarity between nodes. Further, this mechanism is beneficial to gradient backpropagation through the GRU sequence across layers. Experiment results conducted on two benchmark datasets show that our DGGN achieves a comparable performance to the-state-of-art methods.