<ul><li>Researchers investigate deep morphological neural networks (DMNNs) and emphasize the importance of activations between layers.</li><li>They introduce new architectures for DMNNs with different parameter constraints, showcasing successful training and improved pruning capabilities compared to linear networks.</li><li>This study is the first successful attempt to train DMNNs under specific constraints, although the networks' generalization capabilities are limited.</li><li>Additionally, a hybrid network architecture combining linear and morphological layers is proposed, demonstrating faster convergence of gradient descent with large batches.</li></ul>

Training Deep Morphological Neural Networks as Universal Approximators

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