Edge-labeling Graph Neural Network for Few-shot Learning (1905.01436v1)

Abstract: In this paper, we propose a novel edge-labeling graph neural network (EGNN), which adapts a deep neural network on the edge-labeling graph, for few-shot learning. The previous graph neural network (GNN) approaches in few-shot learning have been based on the node-labeling framework, which implicitly models the intra-cluster similarity and the inter-cluster dissimilarity. In contrast, the proposed EGNN learns to predict the edge-labels rather than the node-labels on the graph that enables the evolution of an explicit clustering by iteratively updating the edge-labels with direct exploitation of both intra-cluster similarity and the inter-cluster dissimilarity. It is also well suited for performing on various numbers of classes without retraining, and can be easily extended to perform a transductive inference. The parameters of the EGNN are learned by episodic training with an edge-labeling loss to obtain a well-generalizable model for unseen low-data problem. On both of the supervised and semi-supervised few-shot image classification tasks with two benchmark datasets, the proposed EGNN significantly improves the performances over the existing GNNs.

Edge-Labeling Graph Neural Network for Few-shot Learning

The research paper titled "Edge-Labeling Graph Neural Network for Few-shot Learning" presents an innovative approach to improving few-shot learning through a novel Edge-Labeling Graph Neural Network (EGNN). The primary motivation for this work arises from the limitations observed in existing Graph Neural Network (GNN) methodologies that leverage a node-labeling framework, which often results in implicit modeling of intra- and inter-cluster relationships. This paper proposes an alternative strategy centered on learning edge-labels, thereby providing a more explicit mechanism for capturing intra-cluster similarity and inter-cluster dissimilarity.

Key Contributions

1. Edge-Labeling Mechanism: Unlike node-labeling frameworks, EGNN specializes in directly predicting edge labels. This allows the model to better utilize relational structures in the dataset by explicitly defining the similarity and dissimilarity between node pairs. By iteratively updating these edge labels, the EGNN facilitates a clustering process that dynamically evolves and refines itself as learning progresses.
2. Model Architecture: The EGNN introduces a layered architecture where each layer consists of a node-update and an edge-update block. Node features are adjusted through aggregation of neighbor features modulated by edge weights, and edge features are consistently refined using computed similarities between connected nodes. The integration of both intra-cluster aggregation and inter-cluster disaggregation serves to distribute relational information more effectively across the graph.
3. Training Strategy: The parameters in EGNN are optimized using an episodic training scheme, commonly employed in few-shot learning tasks. This approach is well-suited to enhance model generalization across varied tasks with limited training samples, adapting dynamically to new class distributions without the need for retraining.
4. Transductive Inference Capability: The framework supports both transductive and non-transductive inference, with a demonstrated performance boost in transductive settings. This reflects the model's robustness in scenarios where the entire batch of query samples can be processed concurrently, leveraging inter-query information effectively.
5. Practical and Theoretical Implications: Experimentation on benchmark datasets such as miniImageNet and tieredImageNet has shown significant performance improvements over traditional GNNs. The explicit modeling of edge relationships proves advantageous in few-shot image classification, pushing the boundaries of current methodologies. The EGNN showcases particular adeptness in semi-supervised settings, where some support samples remain unlabeled, reflecting the robustness of edge-based methods in low-data scenarios.

Implications and Future Directions

The implications of this research are multifaceted. Practically, the methodology offers an adaptable framework that can handle variations in task structure, such as differing numbers of classes during training and testing phases—this is particularly valuable for applications where dynamic class compositions are frequent. Theoretically, the shift from node-centric to edge-centric learning invites new inquiries into the benefits of relational learning in domains beyond classification, potentially extending to areas like clustering and network analysis.

Future explorations may delve into refining the edge feature update mechanisms or leveraging graph sparsity techniques to enhance computational efficiency. Moreover, embedding this framework into applications requiring real-time adaptability to novel tasks could further demonstrate its utility and versatility.

The EGNN sets a precedent for rethinking the ways in which relational data structures are utilized and opens pathways for integrating more expressive and flexible representations in machine learning models, particularly in resource-constrained learning scenarios such as few-shot learning.