MorphPool: Efficient Non-linear Pooling & Unpooling in CNNs

• URL: http://arxiv.org/abs/2211.14037v1
• Date: Fri, 25 Nov 2022 11:25:20 GMT
• Title: MorphPool: Efficient Non-linear Pooling & Unpooling in CNNs
• Authors: Rick Groenendijk, Leo Dorst, and Theo Gevers
• Abstract summary: Pooling is essentially an operation from the field of Mathematical Morphology, with max pooling as a limited special case. In addition to pooling operations, encoder-decoder networks used for pixel-level predictions also require unpooling. 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.
• Score: 9.656707333320037
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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