Max-plus operators applied to filter selection and model pruning in neural networks

DOI10.1007/978-3-030-20867-7_24zbMath1445.68326arXiv1903.08072OpenAlexW2921291554MaRDI QIDQ2189383

Isabelle Bloch, Santiago Velasco-Forero, Samy Blusseau, Jesús Angulo, Yunxiang Zhang

Publication date: 15 June 2020

Full work available at URL: https://arxiv.org/abs/1903.08072

zbMATH Keywords

mathematical morphologydeep learningmorphological neural networksmax-plus operatormodel pruningfilter selection

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (10)

Toward a sparsity theory on weighted lattices ⋮ Learning grayscale mathematical morphology with smooth morphological layers ⋮ MorphoActivation: generalizing ReLU activation function by mathematical morphology ⋮ Logarithmic morphological neural nets robust to lighting variations ⋮ Approximating morphological operators with part-based representations learned by asymmetric auto-encoders ⋮ Some open questions on morphological operators and representations in the deep learning era. A personal vision ⋮ On some associations between mathematical morphology and artificial intelligence ⋮ Going beyond p-convolutions to learn grayscale morphological operators ⋮ Sparse approximate solutions to max-plus equations ⋮ Learnable Empirical Mode Decomposition based on Mathematical Morphology

Uses Software

• TensorFlow
• GitHub
• ThiNet

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