mlsanityData to 14 Oct 2026

MorphPool: Efficient Non-linear Pooling & Unpooling in CNNs

arXiv 2211.14037 ↗First posted 25 Nov 2022cs.CV cs.LGcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 17 of its 43 references that are in the map. First posted on arXiv 25 Nov 2022.

Builds on

17 of 43 references.

References · 17

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5 more
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Cited by year

Citing papers per year: 2023: 3.

3 papers.

  1. 2023Discrete Morphological Neural Networkscited by 3
  2. 2023An Algorithm to Train Unrestricted Sequential Discrete Morphological Neural Networkscited by 2
  3. 2023HaarNet: Large-scale Linear-Morphological Hybrid Network for RGB-D Semantic Segmentationcited by 0