mlsanityData to 7 Oct 2026

Pooling Methods in Deep Neural Networks, a Review

arXiv 2009.07485 ↗First posted 16 Sep 2020cs.CV cs.LGcited by 52

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Abstract (arXiv)

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.

Key facts. Cited by 52 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Sep 2020.

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Citing papers per year: 2022: 9; 2023: 11; 2024: 14; 2025: 13; 2026: 5.

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  11. 2025A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEscited by 3
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  12. 2024An In-Depth Analysis of Data Reduction Methods for Sustainable Deep Learningcited by 1
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