# Background-Filtering Feature-Enhanced Graph Neural Networks for Few-Shot Learning
## 4 Experiments
### Ablation Experiment  
Table 4 and Figure 5d presents a study that demonstrates the significant influence of the feature-enhanced module (FE module) and background-filtering module (BF module) on model performance. In the 1-shot context, the model's classification effect falls well short of EGNN, an advanced technique that uses a dual-graph structure, when no additional modules are used. This discrepancy is quite noticeable. This is mostly because, due to its novel dual-graph structure approach, EGNN is able to gather and incorporate a greater amount of sample side data, producing more thorough sample feature updates.  
The BFFE-GNN model performs somewhat worse than EGNN in the 1-shot scenario when the background-filtering module (BF module) is introduced alone, but it catches up to EGNN in the 5-shot situation. This finding demonstrates that background dilution successfully lessens complicated background interference on classification tasks; nonetheless, its benefits under conditions of extremely low sample size have to be completely explored.  
Regarding the independent function of the feature-enhanced module (FE module), in both 1-shot and 5-shot scenarios, the BFFE-GNN model outperforms EGNN by a considerable margin. This demonstrates beyond a doubt how important the FE module is to the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mode** & **1-Shot** & **5-Shot** \\ \hline BF + FE & **61.83 \(\pm\) 0.53** & **79.01 \(\pm\) 0.47** \\ BF & 58.70 \(\pm\) 0.52 & 76.5 \(\pm\) 0.45 \\ FE & 59.85 \(\pm\) 0.52 & 77.82 \(\pm\) 0.47 \\ None & 57.9 \(\pm\) 0.51 & 75.51 \(\pm\) 0.44 \\ \hline \hline \end{tabular}
\end{table}
Table 4: 1-shot and 5-shot classification results on mini-ImageNet dataset under 5-way setting. Top results are highlighted.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mode** & **1-Shot** & **5-Shot** \\ \hline TPN [11] & 59.91 \(\pm\) 0.94 & 73.30 \(\pm\) 0.75 \\ EGNN [12] & 63.52 \(\pm\) 0.52 & 80.24 \(\pm\) 0.49 \\ Meta-SGD [40] & 62.95 \(\pm\) 0.03 & 79.34 \(\pm\) 0.06 \\ Dynamic [5] & 50.90 \(\pm\) 0.46 & 66.69 \(\pm\) 0.36 \\ LEO [41] & 66.33 \(\pm\) 0.0 & 81.44 \(\pm\) 0.09 \\ BFFE-GNN (Ours) & **67.11 \(\pm\) 0.52** & **83.40 \(\pm\) 0.43** \\ \hline \hline \end{tabular}
\end{table}
Table 2: 1-shot and 5-shot classification results on the tiered-ImageNet dataset under the 5-way setting. Top results are highlighted.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mode** & **1-Shot** & **5-Shot** \\ \hline GNN & 40.28 & 54.53 \\ EGNN & 39.78 \(\pm\) 0.47 & 53.65 \(\pm\) 0.48 \\ BFFE-GNN (Ours) & **40.72 \(\pm\) 0.51** & **58.68 \(\pm\) 0.52** \\ \hline \hline \end{tabular}
\end{table}
Table 3: 1-shot and 5-shot classification results on the Tool dataset under 5-way setting. Top results are highlighted.  
edge update procedure. It significantly raises the model's capacity for feature expression and classification accuracy by enhancing the recognition and application of feature gaps.  
Remarkably, the BFFE-GNN model outperforms EGNN in both 1-shot and 5-shot scenarios when the background-filtering module and the feature-enhanced module cooperate. This exceptional result achieves the extraordinary effect of \"\(1+1>2\)\" and confirms the beneficial role of background desalination in feature improvement. It also shows the strong complementarity and synergy between the two modules.