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arXiv · 2211.14037

MorphPool: Efficient Non-linear Pooling & Unpooling in CNNs

Rick Groenendijk·Leo Dorst·Theo Gevers
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Abstract

Pooling is essentially an operation from the field of Mathematical Morphology, with max pooling as a limited special case. The more general setting of MorphPooling greatly extends the tool set for building neural networks. In addition to pooling operations, encoder-decoder networks used for pixel-level predictions also require unpooling. It is common to combine unpooling with convolution or deconvolution for up-sampling. However, using its morphological properties, unpooling can be generalised and improved. Extensive experimentation on two tasks and three large-scale datasets shows that morphological pooling and unpooling lead to improved predictive performance at much reduced parameter counts.

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Rick Groenendijk, Leo Dorst, Theo Gevers. 2022-11-25. MorphPool: Efficient Non-linear Pooling & Unpooling in CNNs. https://arxiv.org/abs/2211.14037

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