Training morphological neural networks with gradient descent: some theoretical insights
<|reference_start|>Training morphological neural networks with gradient descent: some theoretical insights: Morphological neural networks, or layers, can be a powerful tool to boost the progress in mathematical morphology, either on theoretical aspects such as the representation of complete lattice operators, or in the development of image processing pipelines. However, these architectures turn out to be difficult to train when they count more than a few morphological layers, at least within popular machine learning frameworks which use gradient descent based optimization algorithms. In this paper we investigate the potential and limitations of differentiation based approaches and back-propagation applied to morphological networks, in light of the non-smooth optimization concept of Bouligand derivative. We provide insights and first theoretical guidelines, in particular regarding initialization and learning rates.<|reference_end|>
@article{blusseau2024training,
    title={Training morphological neural networks with gradient descent: some
  theoretical insights},
    author={Samy Blusseau (CMM)},
    journal={IAPR Third International Conference on Discrete Geometry and
  Mathematical Morphology, Andrea Frosini; Elena Barcucci; Elisa Pergola;
  Michela Ascolese; Niccol{\'o} Di Marco; Simone Rinaldi; Sara Brunetti; Giulia
  Palma; Veronica Gierrini; Leonardo Bindi, Apr 2024, Firenze, Italy.
  pp.229-241},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12975},
    primaryClass={cs.CV cs.LG stat.ML}
}