Geometric Back-Propagation in Morphological Neural Networks
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 m...
| Publié dans: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 45(2023), 11 vom: 12. Nov., Seite 14045-14051 |
|---|---|
| Auteur principal: | |
| Autres auteurs: | , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2023
|
| Accès à la collection: | IEEE transactions on pattern analysis and machine intelligence |
| Sujets: | Journal Article |
| Accès en ligne |
Volltext |