# A Novel Hierarchical Adaptive Feature Fusion Method for Meta-Learning
## 4 Experimental Results
### Results and Discussion  
We conduct experiments on the miniImageNet dataset. The number of random seeds was fixed to ensure the verifiability of the experiment. The particle swarm optimization algorithm was used to optimize the weight of adaptive feature fusion on the meta-evaluation set. The population was set up with 10 individuals, and the iterations were set at 30 generations. After finding the optimal weight, it was used for the meta-testing. The results of the experiment are shown in Table 1 In the 1-shot strategy, the proposed method results are similar to the state-of-the-art MetaOptNet [38] and better than GEFS [23]. The proposed method outperforms all others in the five-shot strategy. Experiments show that the method is both effective and reliable. To demonstrate the effectiveness of the particle swarm optimization algorithm in weight optimization, each generation's optimal feature fusion weights found on the meta-evaluation set are used for meta-testing. Figure 7 shows the relationship between the maximum accuracy and average accuracy of miniImageNet and the generation in meta-testing under the one-shot and five-shot strategies. The particle swarm optimization algorithm has been found to be both effective and reliable. The method is validated on FC100 and CIFAR-FS. The population was set up with 10 individuals, and the iterations were set at 30 generations. The results of the experiment \begin{table}
\begin{tabular}{c 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 MAML [17] & 32-32-32-32 & 48.70 \(\pm\) 1.84 & 63.11 \(\pm\) 0.92 \\ Matching Networks [10] & 64-64-64-64 & 43.56 \(\pm\) 0.84 & 55.31 \(\pm\) 0.73 \\ IMP [39] & 64-64-64-64 & 49.2 \(\pm\) 0.7 & 64.7 \(\pm\) 0.7 \\ Prototypical Networks [8] & 64-64-64-64 & 49.42 \(\pm\) 0.78 & 68.20 \(\pm\) 0.66 \\ TAML [18] & 64-64-64-64 & 51.77 \(\pm\) 1.86 & 66.05 \(\pm\) 0.85 \\ SAML [40] & 64-64-64-64 & 52.22 \(\pm\) n/a & 66.49 \(\pm\) n/a \\ GCR [41] & 64-64-64-64 & 53.21 \(\pm\) 0.80 & 72.34 \(\pm\) 0.64 \\ KTN [42] & 64-64-64-64 & 54.61 \(\pm\) 0.80 & 71.21 \(\pm\) 0.66 \\ PARN [43] & 64-64-64-64 & 55.22 \(\pm\) 0.84 & 71.55 \(\pm\) 0.66 \\ Dynamic Few-shot [44] & 64-64-128-128 & 56.20 \(\pm\) 0.86 & 73.00 \(\pm\) 0.64 \\ Relation Networks [9] & 64-64-128-128 & 50.44 \(\pm\) 0.82 & 65.32 \(\pm\) 0.70 \\ R2D2 [33] & 96-192-384-512 & 51.2 \(\pm\) 0.6 & 68.8 \(\pm\) 0.1 \\ SNAIL [45] & ResNet-12 & 55.71 \(\pm\) 0.99 & 68.88 \(\pm\) 0.92 \\ AdaResNetNet [46] & ResNet-12 & 56.88 \(\pm\) 0.62 & 71.94 \(\pm\) 0.57 \\ TADAM [34] & ResNet-12 & 58.50 \(\pm\) 0.30 & 76.70 \(\pm\) 0.30 \\ Shot-Free [47] & ResNet-12 & 59.04 \(\pm\) n/a & 77.64 \(\pm\) n/a \\ TEWAM [48] & ResNet-12 & 60.07 \(\pm\) n/a & 75.90 \(\pm\) n/a \\ MTL [49] & ResNet-12 & 61.20 \(\pm\) 1.80 & 75.50 \(\pm\) 0.80 \\ Variational FSL [50] & ResNet-12 & 61.23 \(\pm\) 0.26 & 77.69 \(\pm\) 0.17 \\ MetaOptNet [38] & ResNet-12 & 62.64 \(\pm\) 0.61 & 78.63 \(\pm\) 0.46 \\ Diversity/Cooperation [51] & ResNet-18 & 59.48 \(\pm\) 0.65 & 75.62 \(\pm\) 0.48 \\ Fine-tuning [52] & WRN-28-10 & 57.73 \(\pm\) 0.62 & 78.17 \(\pm\) 0.49 \\ LEO-trainval [53] & WRN-28-10 & 61.76 \(\pm\) 0.08 & 77.59 \(\pm\) 0.12 \\ GEFS [23] & ResNet-12 & 62.02 \(\pm\) 0.63 & 79.64 \(\pm\) 0.44 \\ Ours & ResNet-12 & **62.55 \(\pm\) 0.81** & **80.57 \(\pm\) 0.57** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experiments were conducted on the miniImageNet dataset and our approach was compared with existing methods. Average meta-learning classification accuracies (%) with 95% confidence intervals on miniImageNet meta-test splits. a-b-c-d denotes the size of the layer in the backbone. are shown in Table 2. The results of the one-shot strategy outperforms GEFS [23] for CIFAR-FS, whereas the results of the five-shot strategy achieve the best performance. For FC100, our approach achieved the best performance for both one-shot and five-shot strategies.