# Background-Filtering Feature-Enhanced Graph Neural Networks for Few-Shot Learning
## 1 Introduction  
Deep learning has faced difficulties recently because of its strong reliance on data. Due to professional hurdles and information confidentiality, labeled data collection is typically challenging [1; 2; 3]. In addition, this phenomena shows that few-shot learning with limited training data is feasible given that people can pick up new information fast from a small number of examples. Due to the restricted labeling, there was few-shot learning [4]. Samples are used to forecast unlabeled data, a technique that has gained significant attention because it reduces deep learning's reliance on massive volumes of supervised data. Early on, the main strategy behind few-shot learning was figuring out how to address the overfitting issue brought on by sparse training data [5]. Researchers have used a variety of approaches to address the aforementioned issues. During model training, several of them employ reference regularization to keep the model from depending too much ontraining data. Yet, data augmentation technology is also extensively employed. By applying several random changes to the initial training data, data enhanced efficiently increases the limited sample dataset, which enhances the model's capacity for generalization [6] and increases the accuracy of the model's categorization. Recently, meta-learning has been the main focus of few-shot learning research. The model can learn to learn and pick up content in diverse subjects quickly thanks to meta-learning, which often uses situational training methodologies to learn general knowledge in several fields from a variety of simulated activities. Deep meta-learning is starting to gain more attention and research as deep learning gains popularity. In order to create a mathematical model for learning tasks, deep meta-learning [7] leverages a deep neural network as the backbone network. It then optimizes model parameters using meta-learning techniques to provide a target model that can be used in numerous situations [8]. Novel concepts and approaches for resolving few-shot learning issues are offered by the advancement of deep meta-learning. In addition, due to its propensity for managing intricate relationships, graph neural networks (GNNs) have garnered significant interest in the field of deep learning [9]. Through an iterative process of aggregating the complicated connections between neighbor nodes, graph neural networks use a message propagation mechanism to efficiently collect instances. Considering that the relationship between the support set and the query set is highly significant in few-shot learning, GNNs offers a fresh viewpoint and potential solution to this problem. Researchers have proposed many techniques to employ GNNs for few-shot learning [10; 11; 12; 13]. [PERSON] et al. [10], for instance, showed a creative method of creating a network structure that is strongly connected by support set and query set samples when confronted with a novel assignment involving support sets and query sets. These embedded features and label information reflect each input node in this graph. Iteratively updating these nodes' features through the GNN's neighborhood aggregation method allows for the classification of query samples that are not tagged. In a similar vein, [PERSON] et al. [11] introduced the transductive propagation network (TPN), which operates by using node properties that deep neural networks collect. TPN leverages the GNN's aggregation method to increase the few-shot learning's application breadth. Using an edge labeling framework, EGNN [12] explicitly models intra-cluster similarity and inter-cluster dissimilarity, while CML-BGNN [13] builds a memory-augmented network to facilitate continuous learning. The fundamental concept of the GNN-based few-shot learning approach is to identify the label information of unlabeled samples by identifying and exploiting sample similarities.