# A Comparison of Pooling Methods for Convolutional Neural Networks
## 4 Advantages and Disadvantages of Pooling Approaches
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Pooling Methods**} & \multirow{2}{*}{**Architecture**} & \multirow{2}{*}{**Activation Function**} & \multicolumn{3}{c}{**Error Rate of Different Datasets**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Reference**} \\ \cline{3-4} \cline{6-8}  & & & **MNIST** & & **CIFAR-10** & & **CIFAR-100** \\ \hline \multirow{2}{*}{Gated Method} & \multirow{2}{*}{6 Convolutional Layers} & \multirow{2}{*}{RELU} & \multirow{2}{*}{0.29} & \multirow{2}{*}{7.90} & \multirow{2}{*}{33.22} & \(88\%\) (Rotation Angle) \\ \hline \multirow{2}{*}{Mixed Pooling} & \multirow{2}{*}{6 Convolutional Layers} & \multirow{2}{*}{RELU} & \multirow{2}{*}{0.30} & \multirow{2}{*}{8.01} & \multirow{2}{*}{33.35} & \(90\%\) (Translation Angle) \\ \hline \multirow{2}{*}{Max Pooling} & \multirow{2}{*}{6 Convolutional Layers} & \multirow{2}{*}{RELU} & \multirow{2}{*}{0.32} & \multirow{2}{*}{7.68} & \multirow{2}{*}{32.41} & \(93.75\%\) (Scale Multiplier) \\ \hline \multirow{2}{*}{Max + Tree Pooling} & \multirow{2}{*}{6 Convolutional Layers} & \multirow{2}{*}{RELU} & \multirow{2}{*}{0.39} & \multirow{2}{*}{9.28} & \multirow{2}{*}{34.75} & \(91.5\%\) (Sale multiplier) \\ \hline \multirow{2}{*}{Mixed Pooling} & \multirow{2}{*}{6 Convolutional Layers (Without data Augmentation)} & \multirow{2}{*}{RELU} & \multirow{2}{*}{10.41} & \multirow{2}{*}{12.61} & \multirow{2}{*}{37.20} & \(91.5\%\) & [34] \\ \hline \multirow{2}{*}{Stochastic Pooling} & \multirow{2}{*}{3 Convolutional Layers} & \multirow{2}{*}{RELU} & \multirow{2}{*}{0.47} & \multirow{2}{*}{15.26} & \multirow{2}{*}{42.58} & \multirow{2}{*}{----} \\ \hline Average Pooling & \multirow{2}{*}{6 Convolutional Layers} & \multirow{2}{*}{RELU} & \multirow{2}{*}{0.83} & \multirow{2}{*}{19.38} & \multirow{2}{*}{47.18} & \multirow{2}{*}{----} \\ \hline Rank-Based & \multirow{2}{*}{3 Convolutional Layers} & \multirow{2}{*}{RELU} & \multirow{2}{*}{0.56} & \multirow{2}{*}{18.28} & \multirow{2}{*}{46.24} & \multirow{2}{*}{----} \\ \((\text{RAP})\) & & & & & & \\ \hline Rank-Based & \multirow{2}{*}{3 Convolutional Layers (RWP)} & \multirow{2}{*}{RELU} & \multirow{2}{*}{0.56} & \multirow{2}{*}{19.28} & \multirow{2}{*}{48.54} & \multirow{2}{*}{----} \\ \((\text{RSP})\) & & & & & & \\ \hline Rank-Based & \multirow{2}{*}{3 Convolutional Layers (Parametric)} & \multirow{2}{*}{0.59} & \multirow{2}{*}{17.85} & \multirow{2}{*}{45.48} & \multirow{2}{*}{----} \\ Average Pooling (RAP) & & & & & & \\ \hline Rank-Based & \multirow{2}{*}{3 Convolutional Layers (Parametric)} & \multirow{2}{*}{0.56} & \multirow{2}{*}{18.58} & \multirow{2}{*}{45.86} & \multirow{2}{*}{----} \\ Weighted Pooling (RWP) & & & & & & & \\ \hline Rank-Based & \multirow{2}{*}{3 Convolutional Layers (Parametric)} & \multirow{2}{*}{0.53} & \multirow{2}{*}{18.96} & \multirow{2}{*}{47.09} & \multirow{2}{*}{----} \\ Stochastic Pooling (RSP) & & & & & & \\ \hline Rank-Based & \multirow{2}{*}{3 Convolutional Layers (RSP)} & \multirow{2}{*}{0.42} & \multirow{2}{*}{14.26} & \multirow{2}{*}{44.97} & \multirow{2}{*}{----} \\ Average Pooling (RAP) & & & & & & \\ \hline Rank-Based & \multirow{2}{*}{3 Convolutional Layers (RAP)} & \multirow{2}{*}{Leaky RELU} & \multirow{2}{*}{0.58} & \multirow{2}{*}{17.97} & \multirow{2}{*}{45.64} & \multirow{2}{*}{----} \\ Weighted Pooling (RWP) & & & & & & \\ \hline Rank-Based & \multirow{2}{*}{3 Convolutional Layers (RSP)} & \multirow{2}{*}{Leaky RELU} & \multirow{2}{*}{0.56} & \multirow{2}{*}{19.86} & \multirow{2}{*}{48.26} & \multirow{2}{*}{----} \\ Stochastic Pooling (RSP) & & & & & & \\ \hline Rank-Based & \multirow{2}{*}{3 Convolutional Layers (RSP)} & \multirow{2}{*}{Leaky RELU} & \multirow{2}{*}{0.47} & \multirow{2}{*}{13.48} & \multirow{2}{*}{43.39} & \multirow{2}{*}{----} \\ Stochastic Pooling (RSP) & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparing performance of various pooling methods on different standard datasets. \begin{table}