# A Novel Hierarchical Adaptive Feature Fusion Method for Meta-Learning
## 4 Experimental Results
### Ablation Study
#### 4.4.3 Comparison of Different Backbones  
ResNet-12 is used as the backbone in the above experiments. To compare the performance of the algorithm under different backbone networks, ResNet-12 and SEResNet-12 are used in comparative experiments. Figure 6 shows the network structure of ResNet-12 and SEResNet-12. The experimental results are shown in Tables 7 and 8. It can be concluded that the algorithm exhibits better performance under SEResNet-12, indicating that SEResNet-12 better represents the features of the samples.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{2}{c}{**CIFAR-FS 5-Way**} & \multicolumn{2}{c}{**FC100 5-Way**} & \multicolumn{2}{c}{**miniImageNet 5-Way**} \\ \cline{2-6}  & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** \\ \hline NN & 68.9 \(\pm\) 0.86 & 81.82 \(\pm\) 0.67 & 42.41 \(\pm\) 0.75 & 57.92 \(\pm\) 0.74 & 61.03 \(\pm\) 0.83 & 73.85 \(\pm\) 0.65 \\ SVM & 71.32 \(\pm\) 0.88 & 83.96 \(\pm\) 0.75 & 40.89 \(\pm\) 0.75 & 58.43 \(\pm\) 0.76 & 58.16 \(\pm\) 0.78 & 74.58 \(\pm\) 0.64 \\ Cosine & 68.9 \(\pm\) 0.86 & 81.82 \(\pm\) 0.67 & 42.41 \(\pm\) 0.75 & 57.92 \(\pm\) 0.74 & 61.03 \(\pm\) 0.83 & 73.85 \(\pm\) 0.65 \\ LR & **71.91 \(\pm\) 0.85** & **86.77 \(\pm\) 0.63** & **44.16 \(\pm\) 0.77** & **62.17 \(\pm\) 0.72** & **62.55 \(\pm\) 0.81** & **80.57 \(\pm\) 0.57** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation experiments with classifiers. “NN” and “LR” denote nearest neighbor classifier and logistic regression. “Cosine” stands for cosine similarity.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Backbone**} & \multicolumn{2}{c}{**miniImageNet 5-Way**} \\ \cline{3-4}  & & **1-Shot** & **5-Shot** \\ \hline Ours-channels & ResNet-12 & 62.25 \(\pm\) 0.61 & 80.14 \(\pm\) 0.42 \\ Ours-addition & ResNet-12 & 62.16 \(\pm\) 0.58 & 79.83 \(\pm\) 0.51 \\ Ours-fuse & ResNet-12 & **62.55 \(\pm\) 0.81** & **80.57 \(\pm\) 0.57** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different feature fusion methods on the miniImageNet dataset. “Ours-channels” denotes fusing features directly by the same size of channels. “Ours-addition” denotes summing features from different stages and feeding them to other stages. Average meta-learning classification accuracies (%) with 95% confidence intervals on miniImageNet meta-test splits.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Backbone**} & \multicolumn{2}{c}{**CIFAR-FS 5-way**} & \multicolumn{2}{c}{**FC100 5-Way**} \\ \cline{3-5}  & & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** \\ \hline Ours-channels & ResNet-12 & 71.72 \(\pm\) 0.8 & 86.32 \(\pm\) 0.64 & 42.92 \(\pm\) 0.71 & 59.8 \(\pm\) 0.54 \\ Ours-addition & ResNet-12 & 71.67 \(\pm\) 0.72 & 86.21 \(\pm\) 0.59 & 42.81 \(\pm\) 0.75 & 59.42 \(\pm\) 0.67 \\ Ours-fuse & ResNet-12 & **71.91 \(\pm\) 0.85** & **86.77 \(\pm\) 0.63** & **44.16 \(\pm\) 0.77** & **62.17 \(\pm\) 0.72** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of different feature fusion methods on CIFAR-FS and FC100. “Ours-channels” denotes fusing features directly by the same size of channels. “Ours-addition” denotes summing features from different stages and feeding them to other stages. Average meta-learning classification accuracies (%) with 95% confidence intervals on CIFAR-FS and FC100.