|Title:||Morphological convolutional neural network architecture for digit recognition||Authors:||Mellouli, Dorra
Hamdani, Tarek M.
Sanchez-Medina, Javier J.
Ayed, Mounir Ben
Alimi, Adel M.
|UNESCO Clasification:||120304 Inteligencia artificial||Keywords:||Convolutional neural network (CNN)
Deep neural networks (DNNs)
Morphological CNN (Morph-CNN), et al
|Issue Date:||2019||Publisher:||2162-237X||Journal:||IEEE Transactions on Neural Networks and Learning Systems||Abstract:||Deep neural networks have proved promising results in many applications and fields, but they are still assimilated to a black box. Thus, it is very useful to introduce interpretability aspects to prevent the blind application of deep networks. This paper proposed an interpretable morphological convolutional neural network called Morph-CNN for pattern recognition, where morphological operations were incorporated using counter-harmonic mean into the convolutional layer in order to generate enhanced feature maps. Morph-CNN was extensively evaluated on MNIST and SVHN benchmarks for digit recognition. The different tested configurations showed that Morph-CNN outperforms the existing methods.||URI:||http://hdl.handle.net/10553/55092||ISSN:||2162-237X||DOI:||10.1109/TNNLS.2018.2890334||Source:||IEEE Transactions on Neural Networks and Learning Systems [ISSN 2162-237X], v. 30 (9), p. 2876 - 2885|
|Appears in Collections:||Artículos|
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