## 3 Experiments
### Experiments on Public Datasets
#### 3.2.4 Comparison Experiment Compared with Existing Methods  
This paper compares our method with the SOTA methods in recent years, which are mainly divided into two categories: meta-learning-based methods and transfer learning-based methods. The comparison results with these methods are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Backbone**} & \multicolumn{2}{c}{**MiniImageNet**} & \multicolumn{2}{c}{**CIFAR-FS**} & \multicolumn{2}{c}{**CUB**} \\ \cline{3-8}  & & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** \\ \hline \multicolumn{8}{c}{Meta-learning} \\ \hline Relational & Conv4 & 50.44 \(\pm\) 0.82 & 65.32 \(\pm\) 0.70 & 55.00 \(\pm\) 1.00 & 69.30 \(\pm\) 0.80 & 62.45\(\pm\) 0.98 & 76.11\(\pm\) 0.69 \\ MetaOpt & Conv4 & 52.87 \(\pm\) 0.57 & 68.76 \(\pm\) 0.48 & - & - & - & - \\ PN+rot & Conv4 & 53.63 \(\pm\) 0.43 & 71.70 \(\pm\) 0.36 & - & - & - & - \\ CovaMNet & Conv4 & 51.19 \(\pm\) 0.76 & 67.65\(\pm\) 0.63 & - & - & 52.42 \(\pm\) 0.76 & 63.76 \(\pm\) 0.64 \\ DN4 & Conv4 & 51.24 \(\pm\) 0.74 & 71.02 \(\pm\) 0.64 & - & - & 46.84 \(\pm\) 0.81 & 74.92 \(\pm\) 0.64 \\ MeTAL & Conv4 & 52.63 \(\pm\) 0.37 & 70.52 \(\pm\) 0.29 & - & - & - & - \\ HGNN & Conv4 & 55.63 \(\pm\) 0.20 & 72.48 \(\pm\) 0.16 & - & - & 69.02 \(\pm\) 0.22 & 83.20 \(\pm\) 0.15 \\ DFN & Conv4 & 50.21 \(\pm\) 0.64 & 72.20 \(\pm\) 0.51 & - & - & - & - \\ PSST & Conv4 & - & - & 64.37 \(\pm\) 0.33 & 80.42\(\pm\) 0.32 & - & - \\ \hline \multicolumn{8}{c}{Transfer-learning} \\ \hline Baseline++ & Conv4 & 48.24 \(\pm\) 0.75 & 66.43 \(\pm\) 0.63 & - & - & 60.53 \(\pm\) 0.83 & 79.34 \(\pm\) 0.61 \\ Neg-Cosine & Conv4 & 52.84 \(\pm\) 0.76 & 70.41 \(\pm\) 0.66 & - & - & - & - \\ SKD & Conv4 & 48.14 & 66.36 & - & - & - & - \\ CGCS & Conv4 & 55.53 \(\pm\) 0.20 & 72.12 \(\pm\) 0.16 & - & - & - & - \\ Our method & Conv4 & 57.86 \(\pm\) 0.83 & 73.38 \(\pm\) 0.62 & 67.04 \(\pm\) 0.91 & 80.84 \(\pm\) 0.68 & 70.44 \(\pm\) 0.92 & 84.19 \(\pm\) 0.56 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison results (%) of the experiment on three public datasets.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Backbone**} & \multicolumn{2}{c}{**MiniImageNet**} & \multicolumn{2}{c}{**CIFAR-FS**} & \multicolumn{2}{c}{**CUB**} \\ \cline{3-8}  & & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** \\ \hline \multicolumn{8}{c}{Meta-learning} \\ \hline Relational & Conv4 & 57.32 \(\pm\) 0.84 & 72.90 \(\pm\) 0.64 & 66.40 \(\pm\) 0.93 & 80.44 \(\pm\) 0.67 & 70.20 \(\pm\) 0.93 & 83.88 \(\pm\) 0.57 \\ Local & Conv4 & 57.65 \(\pm\) 0.83 & 73.06 \(\pm\) 0.64 & 66.63 \(\pm\) 0.93 & 80.64 \(\pm\) 0.67 & 70.12 \(\pm\) 0.93 & 83.66 \(\pm\) 0.57 \\ Global-Local & Conv4 & 57.86 \(\pm\) 0.83 & 73.38 \(\pm\) 0.62 & 67.04 \(\pm\) 0.91 & 80.84 \(\pm\) 0.68 & 70.44 \(\pm\) 0.92 & 84.19 \(\pm\) 0.56 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results (%) of ablation experiment.  
According to the results in Table 2, the following observations can be made:  
1. On the MiniImageNet dataset, our proposed method achieves the best classification performance. Compared with the best-performing method in the meta-learning-based category, HGNN, our method outperforms it by 2.23% and 0.9% on 1-shot and 5-shot classification tasks, respectively. In the transfer learning-based category, compared with the best performing method, CGCS, our method outperforms it by 2.33% and 1.26% on 1-shot and 5-shot classification tasks, respectively.
2. On the CIFAR-FS dataset, our proposed method also achieves top performance. Our method outperforms the best-performing method, PSST, by 2.67% and 0.42% on 1-shot and 5-shot classification tasks, respectively.
3. On the CUB-200-2011 dataset, our proposed method achieves the highest classification performance. Our method outperforms the best-performing method, HGNN, by 1.42% and 0.99% on 1-shot and 5-shot classification tasks, respectively.