# Fused Node-Level Residual Structure Edge Graph Neural Network for Few-Shot Image Classification
## 3 Methods
### EGNN + Node Hierarchical Residual Model Architecture
Moreover, the application of this residual connection strategy to other tasks and domains warrants investigation to assess its generalizability and applicability. Improvements in Training and Optimization: Subsequent research efforts could delve into refining the training and optimization processes to mitigate the likelihood of performance degradation. This could involve fine-tuning hyperparameters or employing more sophisticated regularization strategies to address potential overfitting issues. Furthermore, exploring more effective training methods to enhance model robustness is a promising avenue for future exploration. The image classification accuracy of EGNN-NHR0 on mini-ImageNet-5-way-5-shot can be seen from Figure 4, and the image classification accuracy of EGNN-NHR on mini-ImageNet-5-way-5-shot can be seen from Figures 5 and 6, it can be concluded that as the number of layers increases, the accuracy also increases. All of the above points of discussion show improvements in both transductive and non-transductive settings. In particular, EGNN-NHR0 achieved a 1.37% improvement in the mini-ImageNet 5-way 1-shot setting at level L5. In the self-created CBAC dataset, as shown in Table 3, to demonstrate the significant improvement of the residual structure on EGNN, a dataset was created for classifying the

Figure 4: Image classification accuracy of EGNN-NHR\({}_{0}\) on mini-ImageNet-5-way-5-shot. Figure 5: Image classification accuracy of EGNN-NHR on mini-ImageNet-5-way-5-shot. appearances of car brands. This dataset consists of 20,254 color images, categorized into 22 classes. Comparative experiments were conducted on three models, and it was evident that there were notable differences among these models in this small dataset. Specifically, EGNN-NHR0 achieved 60.61% and 66.88% accuracy in the 1-shot and 5-shot settings, respectively, which is a relative improvement of 11.14% and 9.3% compared to the EGNN. To ensure the robustness of this result, comparative experiments were conducted with a GNN model on this dataset. In explaining possible underlying principles, this could be related to the residual connections in the model architecture and the nature of few-shot learning. 1. More improvement with convolutional residual connections in the 5-way 1-shot experiment: This phenomenon might be due to the extremely limited data in the 5-way 1-shot task, where there was only one sample per class for training. Convolutional Residual Connections are better at capturing local features and patterns, making them beneficial for improving model performance in low-data situations. This is because convolutional layers have local receptive fields and are better suited to capturing local patterns, which are more critical in few-shot tasks. 2. More significant improvement with linear residual structures with larger support sets: When the support set becomes larger, the model may need to capture global information and relationships more effectively. Linear residual structures are better at propagating global information, hence leading to more significant improvements with larger support sets. In the 5-way 5-shot task, where each class had five samples, having relatively more data allows the model to learn global relationships better. 3. Hierarchical design of different structural levels: If you design different levels of residual connections intelligently, you can adapt to support sets of different sizes more effectively. At lower model layers, using convolutional residual connections can help extract local features, while at higher layers, using linear residual connections can capture global relationships better. This hierarchical design leverages the advantages of different structures and enhances performance in various data contexts. In summary, this subtle phenomenon may be attributed to the need for the model to balance the learning of local features and global relationships in few-shot learning tasks. Different residual connection structures have different advantages in various tasks and data sizes.