# AFGN: Adaptive Filtering Graph Neural Network for Few-Shot Learning
## 3 Results
### Comparison with Methods  
#### 3.3.1 Experiments on miniImageNet  
We compare the experimental results on the miniImageNet dataset with other state-of-the-art methods to evaluate the model performance. Specifically, in the M-way K-shot, M is set to 5, K is set to 1 and 5, respectively. The experiments are conducted on these two different few-shot task settings, and the experimental results for comparison are calculated with a 95% confidence interval. The experimental results are shown in Table 2. It is apparent from the table that our model performs best in few-shot classification tasks, whether in 5-way 1-shot or 5-way 5-shot. Compared to the second-ranking model, AFGN has improved 3.13% and 5.37% in the 5-way 1-shot and 5-way 5-shot tasks, respectively. Moreover, compared to other graph-based models, our model performs better. Specifically, in 5-way 1-shot tasks, compared to GCN-FL, TPN, EGNN, and HDGN, our model has improved by 15.97%, 10.79%, 7.69%, and 7.17%, respectively. In 5-way 5-shot, our model has improved by 18.22%, 14.77%, 8.29% and 6.48%, respectively. The HDGN presented here is a recently proposed few-shot model. Although HDGN also converts graph convolution into filtering of graph signals in the frequency domain, the difference lies in that HDGN considers low-frequency information more conducive to few-shot classification tasks. By enhancing the low-frequency components and suppressing the high-frequency components, HDGN constructs a low-pass filter for the model. Compared to HDGN which focuses only on low-frequency information, AFGN can more effectively improve the model's performance in classification tasks by adjusting the filter for obtaining frequency components according to different few-shot tasks. The miniImageNet dataset includes a variety of objects and scenes with high visual complexity. Therefore, in the 5-way 1-shot setting, the number of samples per class in the support set is only one, resulting in limited meta-knowledge learned by the model and generally lower prediction accuracy on the query set. However, in the 5-way 5-shot experiment, as the number of samples in the support set increases, the prediction accuracy on the query set also improves. Our model has a stronger generalization ability to unseen classes compared to other models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **5-Way 1-Shot** & **5-Way 5-Shot** \\ \hline MAML [10] & \(48.70\pm 1.84\) & \(55.31\pm 0.73\) \\ GCN-FL [5] & \(50.33\pm 0.36\) & \(66.41\pm 0.63\) \\ RelationNet [19] & \(50.44\pm 0.82\) & \(65.32\pm 0.70\) \\ R2D2 [20] & \(51.20\pm 0.60\) & \(68.02\pm 0.60\) \\ [PERSON] et al. [21] & \(53.14\pm 1.06\) & \(71.48\pm 0.72\) \\ Global [22] & \(53.21\pm 0.40\) & \(72.34\pm 0.41\) \\ MMN [23] & \(53.37\pm 0.48\) & \(66.97\pm 0.35\) \\ FEAT [24] & \(55.15\pm 0.20\) & \(71.61\pm 0.16\) \\ TPN [13] & \(55.51\pm 0.86\) & \(69.86\pm 0.65\) \\ Dynamic [25] & \(56.20\pm 0.86\) & \(71.94\pm 0.57\) \\ SIB [26] & \(58.00\pm 0.60\) & \(70.70\pm 0.40\) \\ TriNet [7] & \(58.12\pm 1.37\) & \(76.92\pm 0.69\) \\ EGNN [14] & \(58.61\pm 0.67\) & \(76.34\pm 0.48\) \\ HDGN [6] & \(59.13\pm 0.65\) & \(78.15\pm 0.41\) \\ TapNet [27] & \(61.65\pm 0.15\) & \(76.36\pm 0.10\) \\ Dense [28] & \(62.53\pm 0.19\) & \(78.95\pm 0.13\) \\ MetaOptNet [29] & \(62.64\pm 0.61\) & \(78.63\pm 0.46\) \\ Meta-Baseline [30] & \(63.17\pm 0.23\) & \(79.26\pm 0.17\) \\ AFGN & \(\mathbf{66.30\pm 0.54}\) & \(\mathbf{84.63\pm 0.28}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison to prior work on miniImageNet. 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.