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- W2911437338 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." @default.
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- W2911437338 date "2019-09-01" @default.
- W2911437338 modified "2023-10-17" @default.
- W2911437338 title "Morphological Convolutional Neural Network Architecture for Digit Recognition" @default.
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- W2911437338 doi "https://doi.org/10.1109/tnnls.2018.2890334" @default.
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