mlsanityData to 7 Oct 2026

Learning Deep Morphological Networks with Neural Architecture Search

arXiv 2106.07714 ↗First posted 14 Jun 2021cs.CV cs.AI cs.LGcited by 6

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Abstract (arXiv)

Deep Neural Networks (DNNs) are generated by sequentially performing linear and non-linear processes. Using a combination of linear and non-linear procedures is critical for generating a sufficiently deep feature space. The majority of non-linear operators are derivations of activation functions or pooling functions. Mathematical morphology is a branch of mathematics that provides non-linear operators for a variety of image processing problems. We investigate the utility of integrating these operations in an end-to-end deep learning framework in this paper. DNNs are designed to acquire a realistic representation for a particular job. Morphological operators give topological descriptors that convey salient information about the shapes of objects depicted in images. We propose a method based on meta-learning to incorporate morphological operators into DNNs. The learned architecture demonstrates how our novel morphological operations significantly increase DNN performance on various tasks, including picture classification and edge detection.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 44 of its 89 references that are in the map. First posted on arXiv 14 Jun 2021.

Builds on

44 of 89 references.

References · 44

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32 more
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Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 3.

6 papers.

  1. 2023An Algorithm to Train Unrestricted Sequential Discrete Morphological Neural Networkscited by 2
  2. 2023The Lattice Overparametrization Paradigm for the Machine Learning of Lattice Operatorscited by 1
  3. 2025Training Deep Morphological Neural Networks as Universal Approximatorscited by 1
  4. 2025Single Domain Generalization for Alzheimer's Detection from 3D MRIs with Pseudo-Morphological Augmentations and Contrastive Learningcited by 1
  5. 2024NAS-BNN: Neural Architecture Search for Binary Neural Networkscited by 0
  6. 2025Morphological Perceptron with Competitive Layer: Training Using Convex-Concave Procedurecited by 0