# 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} \hline \hline
**Type of Pooling** & \multicolumn{2}{c}{**Advantages**} & \multicolumn{2}{c}{**Drawbacks**} & **References** \\ \hline Pyramid Pooling & - & Flexibility to manage input of any size. & - & Deep networks’ training & \\ - & Spatial bins with multiple levels. & & step involves complex & [43] \\  & - & Responsiveness to the image scales of an input. & & implementation. & \\ \hline  & - & Stochastic procedure. & - & Complicated to interpret. & \\  & - & It is conceivable to use non-maximal & - & Extraneous to words & \\  & - & activations. & & negative activations. & \\ Stochastic & - & Feasibility of integrating any regularization & - & Due to the lack of training & \\ Pooling & method, including dropouts, data & & data, overfitting occurs & \\  & augmentation, loss tangent, etc. & & because strong activations & \\  & - & There seems to be no hyper-parameter & & primarily work in \\  & - & to specify. & & process updating. & \\  & - & Lower computational complexity. & - & Scaling challenge. & \\ \hline  & - & Flexible to adapt in nature. & & \\ Tree Pooling & - & Differentiable in perspective among both & - & Inefficient due to thick & \\  & - & parameters as well as inputs. & & layers of the network. & [41; 66] \\  & - & Effective at the network’s lower tiers. & & \\ \hline  & - & Stochastic method. & - & Arbitrarily selecting the & \\  & - & Choice of the pooling region via & & pooling zone significantly & \\  & - & pseudo-randomness or randomness. & & affects model performance & \\ Fractional Max & - & Appropriate use of data augmentation and & & in addition to data & \\ Pooling & - & pseudo-random selection. & & augmentation. & \\  & - & Overlapped rather than disjointed fractional & - & The disjointed fractional & \\  & - & max pooling proved to be more efficient. & & max pooling leads to & \\ \hline  & - & Simple to learn and use. & - & Depending on the design for & \\  & - & Rapid computations while training. & & which it is being employed, & \\  & - & Extrudes in the extent of distortions. & & the grid size should be & \\ S3 Pool & - & Implement data augmentation at the levels of & & adequately specified in each & \\  & & the pooling layer to give it strong & & pooling layer. & \\  & - & generalization performance. & - & A greater grid size & \\  & - & Compared to max pooling, considerably & & potentially results in & \\  & & increases the computational burden. & & increased testing error. & \\ \hline  & & & - & Performance issues can arise & \\  & - & For object recognition tasks, it is implemented. & & while generating lots of & \\  & - & It empowers us to reuse the convolution & - & Computing frequency falls & \\  & network’s feature map. & & short of the expectations. & \\ Rank-Based & - & It provides an opportunity to train object & - & End-to-end training, or & [45] \\ Average Pooling & - & detection systems from beginning to end, & training each aspect of the & \\  & significantly shortening test and & system in one go, is not & \\  & training periods. & & practicable but could & \\  & & produce & & \\  & & much-enhanced results. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._the effectiveness of different pooling approaches on standard datasets including MNIST, CIFAR-10, and CIFAR-100. The architecture and the forms of activation functions that have been used to implement these techniques are presented in the following table. In Table 2, it is shown that for the MNIST dataset, average pooling performed the worst, with an error rate of 0.83%. Furthermore, in comparison to other pooling methods, gated pooling was a significant improvement where the average and maximum pools were responsively combined. With a difference of 0.01%, mixed, tree max average pooling, and fractional max pooling were followed in order by the performance of gated pooling. These pooling strategies' outstanding regularization and generalization capabilities were validated by their effective implementation.