# Few-Shot Image Classification via Mutual Distillation
## 4 Experiments
### Comparison with Other Methods  
We perform an exhaustive comparative examination of our methodology against several contemporary state-of-the-art techniques. These approaches are stratified into three distinct categories of meta-learning: model-based methods, optimization-based methods, and metric-based methods. The detailed results for this comparative evaluation can be found in the respective original publications. A condensed summary of the comparative outcomes is delineated in Tables 3 and 4. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Backbone**} & \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Reference**} & \multicolumn{2}{c}{**5-Way Accuracy (\%)**} \\ \cline{5-6}  & & & & **1-Shot** & **5-Shot** \\ \hline Meta network [5] & Conv & Model & ICML’17 & 49.21 \(\pm\) 0.96 & - \\ \hline SNAIL [30] & ResNet & Model & ICLR’18 & 55.71 \(\pm\) 0.99 & 68.88 \(\pm\) 0.92 \\ \hline MAML [6] & Conv4 & Optimization & ICML’17 & 48.70 \(\pm\) 1.75 & 63.11 \(\pm\) 0.92 \\ \hline Meta-SGD [32] & Conv4 & Optimization & Arvi’17 & 50.47 \(\pm\) 1.87 & 64.03 \(\pm\) 0.94 \\ \hline Reptile [33] & Conv4 & Optimization & Arvi’18 & 49.97 \(\pm\) 0.32 & 65.99 \(\pm\) 0.58 \\ \hline Bilevel Programming [34] & ResNet12 & Optimization & ICML’18 & 50.54 \(\pm\) 0.85 & 64.53 \(\pm\) 0.68 \\ \hline adaResNet [35] & ResNet12 & Optimization & ICML’18 & 56.88 \(\pm\) 0.62 & 71.94 \(\pm\) 0.57 \\ \hline LLAMA [36] & Conv4 & Optimization & ICLR’18 & 49.40 \(\pm\) 1.83 & - \\ \hline DKT [37] & Conv4 & Optimization & NIPS’20 & 49.73 \(\pm\) 0.07 & - \\ \hline BOIL [38] & Conv4 & Optimization & ICLR’21 & 49.61 \(\pm\) 0.16 & 66.45 \(\pm\) 0.37 \\ \hline OVE PG GP + Cosine (ML) [39] & Conv4 & Optimization & ICLR’21 & 50.02 \(\pm\) 0.35 & 64.58 \(\pm\) 0.31 \\ \hline Matching Network [10] & Conv4 & Metric & NIPS’16 & 46.6 & 60.0 \\ \hline Prototypical Network [11] + & ResNet12 & Metric & NIPS’17 & 55.39 \(\pm\) 0.29 & 75.27 \(\pm\) 0.23 \\ \hline Relation Network [12] + & ResNet12 & Metric & CVPR’18 & 56.68 \(\pm\) 0.29 & 71.61 \(\pm\) 0.24 \\ \hline Cross Module [40] & Conv4 & Metric & NIPS’18 & 50.94 \(\pm\) 0.61 & 66.65 \(\pm\) 0.67 \\ \hline PARN [20] & Conv4 & Metric & ICCV’19 & 55.22 \(\pm\) 0.84 & 71.55 \(\pm\) 0.66 \\ \hline DN4 [24] & Conv4 & Metric & CVPR’19 & 51.24 \(\pm\) 0.74 & 71.02 \(\pm\) 0.64 \\ \hline SalNet [41] & Conv4 & Metric & CVPR’19 & 57.45 \(\pm\) 0.80 & 72.01 \(\pm\) 0.75 \\ \hline TPN [16] & ResNet8 & Metric & ICLR’19 & 55.51 \(\pm\) 0.86 & 69.86 \(\pm\) 0.65 \\ \hline SARN [42] & Conv & Metric & ICMEW’19 & 51.62 \(\pm\) 0.31 & 66.16 \(\pm\) 0.51 \\ \hline SAML [43] & Conv & Metric & ICCV’19 & 57.69 \(\pm\) 0.20 & 73.03 \(\pm\) 0.16 \\ \hline L2 AE-D [44] & Conv4 & Metric & Arvi’19 & 54.26 \(\pm\) 0.87 & 70.76 \(\pm\) 0.67 \\ \hline CovaMNet [45] & Conv4 & Metric & AAAI’19 & 51.19 \(\pm\) 0.76 & 67.65 \(\pm\) 0.63 \\ \hline MCGN [46] & 64-96-128-256 & Metric & CVPR’21 & 57.89 \(\pm\) 0.87 & 73.58 \(\pm\) 0.87 \\ \hline
**Our Method** & ResNet12 & Metric & - & **58.09 \(\pm\) 0.30** & **77.33 \(\pm\) 0.22** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative analysis of accuracy with other methods is conducted under 5-way, 1-shot and 5-shot tasks on MiniImageNet. The symbol ‘4’ denotes a result that has been re-implemented, while ‘-’ indicates that the author did not provide the result. The results presented in Table 3 reveal the following comparisons:  
(1) In terms of model-based methodologies, our approach exhibits notable performance advantages, surpassing the Meta Network by over 8.88% in the 1-shot scenario and outperforming SNAIL by margins of 2.38% and 8.45% in the 1-shot and 5-shot scenarios, respectively. (2) Comparisons with optimization-based techniques reveal substantial improvements over classical approaches (MAML, Meta-SGD, Reptile) by up to 10% and 14% in the 1-shot and 5-shot settings, respectively.