# Few-Shot Image Classification via Mutual Distillation
## 4 Experiments
### Comparison with Other Methods
Additionally, our method outperforms LLAMA and DKT by 8.69% and 8.36%, respectively, in the 1-shot setting, and demonstrates clear superiority over BOIL and OVE PG GP + Cosine (ML) by approximately 8% and 12% in the 1-shot and 5-shot settings, respectively. (3) In the metric-based category, our approach showcases significant advancements, surpassing the matching network by 11.49% in the 1-shot task and 17.33% in the 5-shot task, while also demonstrating improvements over prototypical network, relation network, and various state-of-the-art methods (Cross Module, PARN, DN4, SalNet). Furthermore, our method outperforms TPN and MCGN with improvements ranging from 0.64% to 10.68. The outcomes presented in Table 4 for the fine-grained dataset CUB in few-shot image classification highlight the remarkable efficacy of the proposed approach. (1) In the realm of optimization-based methods, specifically OVE PG GP + Cosine (ML), our approach outperforms counterparts by 12.52% and 12.75% in 1-shot and 5-shot accuracies, respectively, with a widening gap compared to antecedent methods. (2) Within metric-based comparisons, our approach surpasses the Prototypical Network by marginal yet consequential increments of 0.09% and 1.89% in the 1-shot and 5-shot settings, respectively. Substantive advancements are evident against the relation network and DeepEMD, with gains ranging from 3.81% to 4.31%, and a notable advantage of 0.85% to 2.6% in the 1-shot and 5-shot settings, achieved without pre-training. (3) In comparisons with formidable contenders like AFHN, K-tuplet, and MsSoSN, our method exhibits a distinctive 5.93% advantage in the 1-shot task and a discernible 3.75% lead in the 5-shot task, all without resorting to methodologies like GAN-based sample generation. \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{3-5}  & & & & **1-Shot** & **5-Shot** \\ \hline MAML [6] * & Conv4 & Optimization & ICML:17 & 38.43 & 59.15 \\ \hline Meta-SGD [32] * & Conv4 & Optimization & Arxiv’17 & 66.9 & 77.10 \\ \hline MACO [31] * & Conv4 & Optimization & Arxiv’18 & 60.76 & 74.96 \\ \hline Meta-LSTM [48] * & Conv4 & Optimization & ICLR:17 & 40.43 & 49.65 \\ \hline DKT [37] & Conv4 & Optimization & ICLR:20 & 63.37 \(\pm\) 0.19 & - \\ \hline OVE PG GP + Cosine (ML) [39] & Conv4 & Optimization & NIPS’20 & 63.98 \(\pm\) 0.43 & 77.44 \(\pm\) 0.18 \\ \hline Matching Network [10] * & Conv4 & Metric & NIPS’16 & 49.34 & 59.31 \\ \hline Prototypical Network [11] \(\dagger\) & ResNet12 & Metric & NIPS’17 & 76.41 \(\pm\) 0.4 & 89.40 \(\pm\) 0.24 \\ \hline Relation Network [12] \(\dagger\) & ResNet12 & Metric & CVPR’18 & 72.69 \(\pm\) 0.49 & 86.98 \(\pm\) 0.28 \\ \hline DN4 [24] & Conv4 & Metric & CVPR’19 & 53.15 \(\pm\) 0.84 & 81.90 \(\pm\) 0.60 \\ \hline DeepEMD [27] & ResNet12 & Metric & CVPR’20 & 75.65 \(\pm\) 0.83 & 88.69 \(\pm\) 0.50 \\ \hline SAML [43] & Conv & Metric & ICCV’19 & 69.33 \(\pm\) 0.22 & 81.56 \(\pm\) 0.15 \\ \hline CovaMNet [45] & Conv4 & Metric & AAAI’19 & 52.42 \(\pm\) 0.76 & 63.76 \(\pm\) 0.64 \\ \hline MSSoSN [49] & Conv4 & Metric & Arxiv’20 & 52.82 & 68.37 \\ \hline K-tuplet [3] & Conv4 & Metric & Neurocomputing’20 & 40.16 \(\pm\) 0.68 & 56.96 \(\pm\) 0.65 \\ \hline AFHN [47] & Restnet12 & Metric & CVPR’20 & 70.53 \(\pm\) 1.01 & 83.95 \(\pm\) 0.63 \\ \hline
**Our Method** & ResNet12 & Metric & - & **76.50 \(\pm\) 0.48** & **91.29 \(\pm\) 0.22** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative analysis of accuracy with other methods is conducted under 5-way, 1-shot and 5-shot tasks on CUB. The symbol ‘\(\star\)’ denotes a result that has been re-implemented, while ‘\(\cdot\)’ indicates that the author did not provide the result. The symbol ‘\(\star\)’ signifies that the result is reported in [47].