Graph Neural Networks with Prototype Nodes for Few-shot Image Classification

Sung-eun Jang, Juntae Kim

http://doi.org/10.5626/JOK.2023.50.2.127

The remarkable performance of deep learning models is based on a large amount of training data. However, there are a number of domains where it is difficult to obtain such a large amount of data, and in these domains a large amount of resources must be invested for data collection and refining. To overcome these limitations, research on few-shot learning, which enables learning with only a small number of data, is being actively conducted. In particular, among meta learning methodologies, metric-based learning which utilizes similarity between data has the advantage that it does not require fine-tuning of the model for a new task, and recent studies using graph neural networks have shown good results. A few-shot classification model based on a graph neural network can explicitly process data characteristics and the relationship between data by constructing a task graph using data of a given support set and query set as nodes. The EGNN(Edge-labeling Graph Neural Net) model expresses the similarity between data in the form of edge labels and models the intra-class and inter-class similarity more clearly. In this paper, we propose a method of applying a prototype node representing each class to few-shot task graph to model the similarity between data and class-data at the same time. The proposed model provides a generalized prototype node that is created based on task data and class configuration, and it can perform two different few-shot image classification predictions based on the prototype-query edge label or the Euclidean distance between prototype-query nodes. Comparing the 5-way 5-shot classification performance on the mini-ImageNet dataset with the EGNN model and other meta-learning-based few-shot classification models, the proposed model showed significant performance improvement.