# Mask Mixup Model: Enhanced Contrastive Learning for Few-Shot Learning
## 5 Experiments
### Comparison with Other Methods  
To assess the performance of our model, we compared it to several previous methods, including ProtoNet [4], infoPatch [8], LSFSL [2], MML [6], TALDS-Net [3], and RENet [11] among others. These methods either represent classical Few-Shot Learning (FSL) approaches or have previously demonstrated the best-reported results. Our findings are presented in Table 1.  
In Table 1, this paper compares miniImageNet and tieredImageNet datasets using Resnet-12 as the backbone for the compared methods. Compared with DeepEMD [5], this paper requires less computation, and compared with RENet [11] and CVET [12], it does not require additional parameters, demonstrating the superiority of the proposed method over others. In Table 2, we compared our method with others on the FC100 dataset, using ResNet-12 as the backbone. In Table 3, the comparison is made on the CUB-200 dataset. Even though the backbone network parameters of some methods are greater than those of this paper, the proposed method still outperforms others. By visualizing spatial correspondences, we confirm our ability to recognize images using partial information. Utilizing the features extracted from the supporting image, we compute the inner product for each segment of the query image. Figure 3 demonstrates our method's superiority in spatial relationship comprehension over the benchmark. Notably, our model accurately and comprehensively covers the foreground and excels in recognizing objects within noisy images without false recognitions elsewhere. This further validates the effectiveness of our model representation. We employed tSNE plots to visualize the embeddings. Specifically, we constructed a larger episode from the target categories within the miniImageNet dataset and fed it into both the baseline model[1] and our complete model. Figure 4 presents the visualizations of the embeddings. From the figure, it is apparent that the images generated by the baseline model lack compactness, with many categories blending together, whereas our method produces images that are more compact, allowing the images of each category to cluster more effectively. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**miniImageNet**} & \multicolumn{2}{c}{**tieredImageNet**} \\  & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** \\ \hline ProtoNet [4] & 62.39 \(\pm\) 0.21 & 80.53 \(\pm\) 0.14 & 68.23 \(\pm\) 0.23 & 84.03 \(\pm\) 0.16 \\ CAN [13] & 63.85 \(\pm\) 0.48 & 79.44 \(\pm\) 0.34 & 69.89 \(\pm\) 0.51 & 84.23 \(\pm\) 0.37 \\ DeepBDC [14] & 67.83 \(\pm\) 0.43 & 84.46 \(\pm\) 0.28 & 72.34 \(\pm\) 0.49 & **87.31 \(\pm\) 0.32** \\ DeepEMD [5] & 65.91 \(\pm\) 0.82 & 82.41 \(\pm\) 0.56 & 71.16 \(\pm\) 0.87 & 86.03 \(\pm\) 0.58 \\ RENet [11] & 67.60 \(\pm\) 0.44 & 82.58 \(\pm\) 0.30 & 71.61 \(\pm\) 0.51 & 85.28 \(\pm\) 0.35 \\ DMF [15] & 67.76 \(\pm\) 0.46 & 82.71 \(\pm\) 0.31 & 71.89 \(\pm\) 0.52 & 85.96 \(\pm\) 0.35 \\ FRN [16] & 66.45 \(\pm\) 0.19 & 82.83 \(\pm\) 0.13 & 72.06 \(\pm\) 0.22 & 86.89 \(\pm\) 0.14 \\ ZN [17] & 67.35 \(\pm\) 0.43 & 83.04 \(\pm\) 0.29 & 72.28 \(\pm\) 0.51 & 87.20 \(\pm\) 0.34 \\ MML [6] & 67.58 \(\pm\) 0.23 & 81.41 \(\pm\) 0.20 & 71.38 \(\pm\) 0.25 & 84.65 \(\pm\) 0.20 \\ infoPatch [8] & 67.67 \(\pm\) 0.45 & 82.44 \(\pm\) 0.31 & 71.51 \(\pm\) 0.52 & 85.44 \(\pm\) 0.35 \\ LSFSL [2] & 64.67 \(\pm\) 0.49 & 81.79 \(\pm\) 0.18 & 71.17 \(\pm\) 0.52 & 86.23 \(\pm\) 0.22 \\ TALDS-Net [3] & 67.89 \(\pm\) 0.20 & 84.31 \(\pm\) 0.44 & 71.34 \(\pm\) 0.32 & 86.12 \(\pm\) 0.33 \\ ours & **70.03 \(\pm\) 0.36** & **84.74 \(\pm\) 0.53** & **73.14 \(\pm\) 0.57** & 86.72 \(\pm\) 0.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: This is a comparison of our method with other methods using ResNet12 as the backbone network, on the miniImageNet and tieredImageNet datasets. Bold indicates the best performance.