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|a 10.1109/TPAMI.2023.3290615
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|a eng
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| 100 |
1 |
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|a Groenendijk, Rick
|e verfasserin
|4 aut
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| 245 |
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|a Geometric Back-Propagation in Morphological Neural Networks
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|c 2023
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 03.10.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a This paper provides a definition of back-propagation through geometric correspondences for morphological neural networks. In addition, dilation layers are shown to learn probe geometry by erosion of layer inputs and outputs. A proof-of-principle is provided, in which predictions and convergence of morphological networks significantly outperform convolutional networks
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|a Journal Article
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|a Dorst, Leo
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| 700 |
1 |
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|a Gevers, Theo
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 11 vom: 12. Nov., Seite 14045-14051
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