  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.
