Maximal function pooling with applications

• URL: http://arxiv.org/abs/2103.01292v1
• Date: Mon, 1 Mar 2021 20:30:04 GMT
• Title: Maximal function pooling with applications
• Authors: Wojciech Czaja, Weilin Li, Yiran Li, Mike Pekala
• Abstract summary: Maxfun pooling is inspired by the Hardy-Littlewood maximal function. It is presented as a viable alternative to some of the most popular pooling functions, such as max pooling and average pooling. We demonstrate the features of maxfun pooling with two applications: first in the context of convolutional sparse coding, and then for image classification.
• Score: 4.446564162927513
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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