Maximal function pooling with applications
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 27 references that are in the map. First posted on arXiv 1 Mar 2021.
Builds on
8 of 27 references.
References · 8
- 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
- 2016Understanding Deep Convolutional Networkscited by 76
- 2016Convolutional Neural Networks Analyzed via Convolutional Sparse Codingcited by 51
- 2013Stochastic Pooling for Regularization of Deep Convolutional Neural Networkscited by 48
- 2015Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Treecited by 36
- 2014Fractional Max-Poolingcited by 35
- 2013Signal Recovery from Pooling Representationscited by 12
- 2018Hartley Spectral Pooling for Deep Learningcited by 6
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Citing papers per year: 2023: 1.
1 paper.