# Fused Node-Level Residual Structure Edge Graph Neural Network for Few-Shot Image Classification
## 1 Introduction  
Deep learning has shown to be quite successful in tackling complicated challenges in the current digital era. However, a sizable amount of labeled training data is often required [1, 2]. Humans, on the other hand, have the amazing capacity to learn quickly from a little amount of material. Few-Shot Learning (FSL) is a concept that was developed in response to how people learn. It seeks to construct models that can perform well even with little labeled data or in situations when there is little data available. Due to this, both the academic and corporate worlds have shifted their attention in recent years to the area of \"few-shot classification\". The difficulty of learning and using new categories or concepts when there is a severe lack of training data is addressed in this field [3, 4, 5, 6, 7, 8, 9, 10]. Researchers have begun to investigate techniques for few-shot picture categorization in the context of deep learning [4, 11, 12, 13]. These techniques aim to educate computers how to generate accurate classification predictions when presented with entirely fresh data and extract information from a small number of labeled cases. While it may be a relatively simple activity for humans, it is nonetheless very difficult for machines to complete. Initially, situations with extremely few accessible samples for each class were used to define few-shot learning [14, 15, 16]. With the development of deep learning, this premise has been expanded to include situations in which there are a large number of accessible base classes and new classes that are confined by methods that need limited data. In the context of meta-learning, where representations are learned on base classes and meta-learningprocedures enable efficient recognition of unseen classes with less training data, this more realistic environment fits the definition of meta-learning. Meta-learners are trained across a variety of related tasks and develop the ability to quickly recognize new, undiscovered classes by making use of the distribution of tasks that are comparable to them. They gather a shared representation from a variety of these jobs, offering improved initialization for tasks requiring classes that are not visible. Graph Neural Networks (GNNs) have been introduced to handle rich relational structures in each recognition task in order to fully use the relationship between the support set and queries in few-shot learning [17; 18]. Through message-passing algorithms, GNNs iteratively aggregate neighboring features to reflect complex interactions between query instances and support instances. GNN approaches increase performance in the setting of few-shot learning by optimizing node and edge update functions to learn class uniqueness and class commonality. For the classification of unlabeled samples, [PERSON] et al. [17] used node-centric GNNs to transport messages across connected nodes. In order to infer correlations between queries and the preexisting support set, [PERSON] et al. [19] iteratively updated edge labels. In order to explicitly model relationships between samples, [PERSON] et al. [14] proposed a method based on node-pair features. Node pairs constitute individual nodes in the relationship graph, and aggregation weights are determined by comparing the similarity between nearby relationship nodes and support or query nodes. To investigate potential hierarchies within categories, [PERSON] et al. [15] constructed hierarchical GNNs. However, the treatment of these issues is extremely complicated by current methods. It is advisable to try simpler approaches to increase accuracy and decrease complexity in order to strike a balance between processing power and efficiency. For machine learning tasks using graph data, EGNN is a Graph Neural Network (GNN) approach based on edge updates. The main goal of EGNNs is to more effectively represent relationships between nodes by modeling the graph's edges.