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Pooling Methods in Deep Neural Networks, a Review

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arxiv 2009.07485 v1 pith:GQS4UME7 submitted 2020-09-16 cs.CV cs.LG

Hossein Gholamalinezhad , Hossein Khosravi This is my paper · ORCID
classification cs.CVcs.LG
keywords poolinglayerneuraldeepmethodsnetworksfeaturemain
verification ladder T0 review T1 audit T2 compute T3 formal
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Nowadays, Deep Neural Networks are among the main tools used in various sciences. Convolutional Neural Network is a special type of DNN consisting of several convolution layers, each followed by an activation function and a pooling layer. The pooling layer is an important layer that executes the down-sampling on the feature maps coming from the previous layer and produces new feature maps with a condensed resolution. This layer drastically reduces the spatial dimension of input. It serves two main purposes. The first is to reduce the number of parameters or weights, thus lessening the computational cost. The second is to control the overfitting of the network. An ideal pooling method is expected to extract only useful information and discard irrelevant details. There are a lot of methods for the implementation of pooling operation in Deep Neural Networks. In this paper, we reviewed some of the famous and useful pooling methods.

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