# Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning
## 4 Results and Discussions
### Comparison with State-of-the-Art Methods
\begin{table}
\begin{tabular}{c l l l l} \hline \hline \multirow{3}{*}{**Setting**} & \multirow{3}{*}{**Method**} & \multicolumn{3}{c}{**cieteredImageNet**} \\  & & **Backbone** & **1-Shot** & **5-Shot** \\ \hline \multirow{8}{*}{Inductive} & ProtoNet [55] & ConvNet4 & \(53.31\pm 0.89\%\) & \(72.69\pm 0.74\%\) \\  & LEO [56] & WRN & \(66.33\pm 0.05\%\) & \(81.44\pm 0.09\%\) \\  & SimpleShot [35] & DenseNet121 & \(71.32\pm 0.22\%\) & \(86.66\pm 0.15\%\) \\  & PT + NCM [1] & DenseNet121 & \(69.96\pm 0.22\%\) & \(86.45\pm 0.15\%\) \\  & FEAT[28] & ResNet12 & \(70.80\pm 0.23\%\) & \(84.79\pm 0.16\%\) \\  & DeepEMD[29] & ResNet12 & \(71.16\pm 0.87\%\) & \(86.03\pm 0.58\%\) \\  & RENet[30] & ResNet12 & \(71.61\pm 0.51\%\) & \(85.28\pm 0.35\%\) \\  & PEM\({}_{\text{b}}\)E-NCM (ours) & DenseNet121 & \(\mathbf{71.86\pm 0.21\%}\) & \(\mathbf{87.09\pm 0.15\%}\) \\ \hline \multirow{8}{*}{Transductive} & BD-CSPN [50] & WRN & \(78.74\pm 0.95\%\) & \(86.92\pm 0.63\%\) \\  & LaplacianShot [51] & DenseNet121 & \(80.30\pm 0.22\%\) & \(87.93\pm 0.15\%\) \\  & MCT [53] & ResNet12 & \(82.32\pm 0.81\%\) & \(87.36\pm 0.50\%\) \\  & TIM-GD [52] & WRN & \(82.10\%\) & \(89.80\%\) \\  & TAFSSL [20] & DenseNet121 & \(84.29\pm 0.25\%\) & \(89.31\pm 0.15\%\) \\  & PT + MAP [1] & DenseNet121 & \(85.75\pm 0.26\%\) & \(90.43\pm 0.14\%\) \\  & PEM\({}_{\text{n}}\)E-BMS (ours) & DenseNet121 & \(85.08\pm 0.25\%\) & \(91.08\pm 0.14\%\) \\  & PEM\({}_{\text{n}}\)E-BMS* (ours) & DenseNet121 & \(\mathbf{86.07\pm 0.25\%}\) & \(\mathbf{91.09\pm 0.14\%}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c l l l l} \hline \hline \multirow{3}{*}{**Setting**} & \multirow{3}{*}{**Method**} & \multicolumn{3}{c}{**cieteredImageNet**} \\  & & **Backbone** & **1-Shot** & **5-Shot** \\ \hline \multirow{8}{*}{Inductive} & Baseline++ [18] & ResNet10 & \(69.55\pm 0.89\%\) & \(85.17\pm 0.50\%\) \\  & MAML [13] & ResNet10 & \(70.32\pm 0.99\%\) & \(80.93\pm 0.71\%\) \\  & ProtoNet [55] & ResNet18 & \(72.99\pm 0.88\%\) & \(86.64\pm 0.51\%\) \\  & Matching Networks [49] & ResNet18 & \(73.49\pm 0.89\%\) & \(84.45\pm 0.58\%\) \\  & FEAT[28] & ResNet12 & \(73.27\pm 0.22\%\) & \(85.77\pm 0.14\%\) \\  & DeepEMD[29] & ResNet12 & \(75.65\pm 0.83\%\) & \(88.69\pm 0.50\%\) \\  & RENet[30] & ResNet12 & \(79.49\pm 0.44\%\) & \(91.11\pm 0.24\%\) \\  & S2M2\_R [2] & WRN & \(80.68\pm 0.81\%\) & \(90.85\pm 0.44\%\) \\  & PT + NCM [1] & WRN & \(80.57\pm 0.20\%\) & \(91.15\pm 0.10\%\) \\  & PEM\({}_{\text{b}}\)E-NCM (ours) & WRN & \(\mathbf{80.82\pm 0.19\%}\) & \(\mathbf{91.46\pm 0.10\%}\) \\ \hline \multirow{8}{*}{Transductive} & LaplacianShot [51] & ResNet18 & \(80.96\%\) & \(88.68\%\) \\  & TIM-GD [52] & ResNet18 & \(82.20\%\) & \(90.80\%\) \\  & BD-CSPN [50] & WRN & \(87.45\%\) & \(91.74\%\) \\  & Transfer + SGC [19] & WRN & \(88.35\pm 0.19\%\) & \(92.14\pm 0.10\%\) \\  & PT + MAP [1] & WRN & \(91.55\pm 0.19\%\) & \(93.99\pm 0.10\%\) \\  & LST + MAP [57] & WRN & \(91.68\pm 0.19\%\) & \(94.09\pm 0.10\%\) \\  & PEM\({}_{\text{n}}\)E-BMS (ours) & WRN & \(91.01\pm 0.19\%\) & \(94.60\pm 0.09\%\) \\  & PEM\({}_{\text{n}}\)E-BMS* (ours) & WRN & \(\mathbf{91.91\pm 0.18\%}\) & \(\mathbf{94.62\pm 0.09\%}\) \\ \hline \multirow{8}{*}{**Setting**} & \multirow{3}{*}{**Method**} & \multicolumn{3}{c}{**CIFAR-FS**} \\  & & **Backbone** & **1-Shot** & **5-Shot** \\ \hline \multirow{8}{*}{Inductive} & ProtoNet [55] & ConvNet64 & \(55.50\pm 0.70\%\) & \(72.00\pm 0.60\%\) \\  & MAML [13] & ConvNet32 & \(58.90\pm 1.90\%\) & \(71.50\pm 1.00\%\) \\  & RENet[30] & ResNet12 & \(74.51\pm 0.46\%\) & \(86.60\pm 0.32\%\) \\ \cline{1-1}  & BD-CSPN [50] & WRN & \(72.13\pm 1.01\%\) & \(82.28\pm 0.69\%\) \\ \cline{1-1}  & S2M2\_R [2] & WRN & \(74.81\pm 0.19\%\) & \(87.47\pm 0.13\%\) \\ \cline{1-1}  & PT + NCM [1] & WRN & \(74.64\pm 0.21\%\) & \(87.64\pm 0.15\%\) \\ \cline{1-1}  & PEM\({}_{\text{b}}\)E-NCM (ours) & WRN & \(\mathbf{74.84\pm 0.21\%}\) & \(\mathbf{87.73\pm 0.15\%}\) \\ \hline \hline \end{tabular}
\end{table}