# AFGN: Adaptive Filtering Graph Neural Network for Few-Shot Learning
## 3 Results
### Ablation Study  
As mentioned earlier, utilizing ResNet as the backbone for few-shot classification tasks has powerful feature extraction capabilities. ResNet offers variations with different depths, including ResNet-12, ResNet-18, ResNet-34, ResNet-50, etc. In few-shot tasks, shallower networks are commonly preferred to prevent overfitting. Therefore, ResNet-12 and ResNet-18 are chosen as the backbone in our model. To study and analyze the impact of different ResNet on the dataset in the model, we replaced ResNet-12 and ResNet-18 in the experiment to observe the performance changes of the model. ResNet-12 is composed of 4 residual blocks, with each block containing three \(3\times 3\) convolutional layers, normalization layer, ReLu activation layer and down-sampling layer. The channels of the 4 residual blocks is 64, 160, 320, and 640 respectively. Similarly, ResNet-18 consists of 8 residual blocks, each block containing two \(3\times 3\) convolutional layers, normalization layer, ReLU activation layer and down-sampling. The channels in the 8 residual blocks are 64, 64, 128, 128, 256, 256, 512, 512 respectively.  
We conducted experiments on the miniImageNet, CUB, and FC100, and the ablation experiment results of the model under the 5-way 1-shot and 5-way 5-shot settings are shown in Tables 5 and 6, respectively. It is obvious from Tables 5 and 6 that, whether it is 5-way 1-shot or 5-way 5-shot, on miniImageNet and FC100, utilizing ResNet-12 as the backbone leads to a higher average accuracy compared to using ResNet-18. Contrary to this, the average prediction accuracy is better for CUB when using ResNet-18 as the backbone compared to utilizing ResNet-12. Through the analysis of data size, we discovered that the data size in CUB are 224, exceeding those in miniImageNet. While a shallower network is required to prevent overfitting, ResNet-18, compared to ResNet-12, can handle more complex images and has stronger feature extraction capabilities.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **5-Way 1-Shot** & **5-Way 5-Shot** \\ \hline MAML [10] & \(38.10\pm 1.70\) & \(50.40\pm 1.00\) \\ TADAM [34] & \(40.10\pm 0.40\) & \(56.10\pm 0.40\) \\ MetaOptNet [29] & \(41.10\pm 0.60\) & \(55.50\pm 0.60\) \\ ProtoNet [12] & \(41.54\pm 0.76\) & \(57.08\pm 0.76\) \\ [PERSON] et al. [35] & \(42.31\pm 0.75\) & \(58.16\pm 0.78\) \\ [PERSON] et al. [36] & \(43.16\pm 0.59\) & \(57.57\pm 0.55\) \\ \(E^{3}BM\)[37] & \(43.20\pm 0.30\) & \(60.20\pm 0.30\) \\ ConstellationNet [38] & \(43.80\pm 0.20\) & \(59.70\pm 0.20\) \\ [PERSON] et al. [39] & \(44.60\pm 0.70\) & \(60.90\pm 0.60\) \\ MixtFSL [40] & \(44.89\pm 0.63\) & \(60.70\pm 0.60\) \\ SIB [26] & \(\mathbf{45.20\pm 0.00}\) & \(55.90\pm 0.00\) \\ AFGN & \(44.50\pm 0.21\) & \(\mathbf{63.27\pm 0.24}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison to prior work on FC100. 5-way 1-shot and 5-way 5-shot average classification accuracy(%) with 95% confidence intervals. The best results in the table are highlighted in bold.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Backbone** & **miniImageNet** & **CUB** & **FC100** \\ \hline ResNet-12 & \(\mathbf{66.30\pm 0.54}\) & \(79.33\pm 0.15\) & \(\mathbf{44.50\pm 0.21}\) \\ ResNet-18 & \(64.82\pm 0.21\) & \(\mathbf{81.10\pm 0.37}\) & \(43.15\pm 0.14\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of different ResNet models on datasets. 5-way 1-shot average classification accuracy(%) with 95% confidence intervals. The best results in the table are highlighted in bold.