# Background-Filtering Feature-Enhanced Graph Neural Networks for Few-Shot Learning
## 4 Experiments
### Experiment and Result Analysis  
We evaluated the suggested model BFFE-GNN against standard models in 5-way-1-shot and 5-way-5-shot environments using mini-ImageNet, tiered-ImageNet, and Tool datasets. All models produced satisfactory outcomes. Table 1 and Figure 4(a) illustrates how our model outperformed EGNN in 1 s and 5 s, with improvements of 2.2% and 2.67%, respectively, on the mini-ImageNet dataset. When the sample background's interference with judgment is reduced and the feature gap is strengthened, as can be seen by comparing the key differences between the two models, our model performs consistently and significantly better in both 1 s and 5 s on the mini-ImageNet dataset. Table 2 and Figure 4(b) illustrates how, in comparison to EGNN, our model performed better in 1s and 5s by 3.59% and 3.16%, respectively, using the tiered-ImageNet dataset. The improvement on the tiered-ImageNet dataset is more substantial than the improvement on the mini-ImageNet dataset, suggesting that our model can perform better than average with a larger dataset and more training rounds. This demonstrates how the model's performance has significantly increased in comparison to 1s with the improvement of the support set data. Table 3 and Figure 4(c) illustrates that, in comparison to EGNN, our model performed better in 1s and 5s by 0.94% and 5.03%, respectively, under the tool dataset. The aforesaid notion is further confirmed by the fact that our model's improvement in 1 s is very minimal in the tool dataset setting. The model may not be able to learn more categories of information gaps during the feature-enhanced training stage, unlike on the mini-ImageNet dataset, due to the small amount of categories in the tool dataset. This would result in a worse generalization ability for the model. Another scenario is that the feature-enhanced training \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **1-Shot** & **5-Shot** \\ \hline GNN [10] & 50.33 \(\pm\) 0.36 & 66.41 \(\pm\) 0.63 \\ TPN [11] & 55.51 \(\pm\) 0.86 & 69.43 \(\pm\) 0.65 \\ EGNN [12] & 59.63 \(\pm\) 0.52 & 76.34 \(\pm\) 0.48 \\ wDAE-GNN [36] & 61.07 \(\pm\) 0.15 & 76.75 \(\pm\) 0.11 \\ SNAIL [37] & 55.71 \(\pm\) 0.99 & 68.88 \(\pm\) 0.92 \\ TADAM [38] & 58.50 \(\pm\) 0.30 & 76.70 \(\pm\) 0.30 \\ Shot-Free [39] & 59.04 \(\pm\) 0.43 & 77.64 \(\pm\) 0.39 \\ BFFE-GNN (Ours) & **61.83 \(\pm\) 0.54** & **79.01 \(\pm\) 0.47** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: 1-shot and 5-shot classification results on the mini-ImageNet dataset under 5-way setting. Top results are highlighted. phase contributes to the loss calculation in some way, which results in less loss contribution to the query set data in the main training at 1 s. In this case, the model's optimization is constrained, but it also offers a good chance for the model to accept. Future advancements offer fresh perspectives. The model's accuracy in the 5s environment has significantly increased, suggesting the model's capacity to return to a more normal level more quickly than in the 1s environment due to the larger number of samples in the support set. The three datasets mentioned above demonstrate the BFFE-GNN model's significant advantage over EGNN as well as its strong generalization capabilities. Nevertheless, the model's is advantage in the Tool dataset is not as great as it is in other datasets, suggesting that there are two potential reasons why the BFFE-GNN model is limited in the presence of monotonous backdrop images and a dearth of known samples. The model's performance  
Figure 5: The performance of different models on various datasets. (**a**) Performance of different models on the mini-ImageNet dataset under the 5-way\(-\)1-shot and 5-shot settings. (**b**) Performance of different models on the tiered-ImageNet dataset under the 5-way\(-\)1-shot and 5-shot settings. (**c**) Performance of different models on the Tool dataset under the 5-way\(-\)1-shot and 5-shot settings.