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.
This paper is dedicated to our friend, Professor John Benedetto, on the occasion of his 80th birthday.
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Czaja, W., Li, W., Li, Y., Pekala, M. (2021). Maximal Function Pooling with Applications. In: Hirn, M., Li, S., Okoudjou, K.A., Saliani, S., Yilmaz, Ö. (eds) Excursions in Harmonic Analysis, Volume 6. Applied and Numerical Harmonic Analysis. Birkhäuser, Cham. https://doi.org/10.1007/978-3-030-69637-5_21
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DOI: https://doi.org/10.1007/978-3-030-69637-5_21
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