# Universal Pooling -- A New Pooling Method for Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1907.11440 · HTML: https://mlsanity.com/paper/1907.11440/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2019.

## Built on by (most cited first, 5 of 5)

- [Kernel function impact on convolutional neural networks](https://mlsanity.com/paper/2302.10266/) (2023)
- [Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration](https://mlsanity.com/paper/2408.02906/) (2024)
- [Data-Driven Gyroscope Calibration](https://mlsanity.com/paper/2410.12485/) (2024)
- [Frequency-Domain Latent Attention Gating for Cross-Domain Token Aggregation](https://mlsanity.com/paper/2606.08191/) (2026)
- [FLaG: Frequency-Domain Latent-attention Gated Pooling for Token Aggregation](https://mlsanity.com/paper/2609.00831/) (2026)

## Abstract (arXiv)

Pooling is one of the main elements in convolutional neural networks. The pooling reduces the size of the feature map, enabling training and testing with a limited amount of computation. This paper proposes a new pooling method named universal pooling. Unlike the existing pooling methods such as average pooling, max pooling, and stride pooling with fixed pooling function, universal pooling generates any pooling function, depending on a given problem and dataset. Universal pooling was inspired by attention methods and can be considered as a channel-wise form of local spatial attention. Universal pooling is trained jointly with the main network and it is shown that it includes the existing pooling methods. Finally, when applied to two benchmark problems, the proposed method outperformed the existing pooling methods and performed with the expected diversity, adapting to the given problem.