  Inspired by the Hardy-Littlewood maximal function, we propose a novel pooling
strategy which is called maxfun pooling. It is presented both as a viable
alternative to some of the most popular pooling functions, such as max pooling
and average pooling, and as a way of interpolating between these two
algorithms. We demonstrate the features of maxfun pooling with two
applications: first in the context of convolutional sparse coding, and then for
image classification.
