Linguistics

Philosophy

Layer (electronics)

Computer science

Numerical digit

Organic chemistry

MNIST database

Pattern recognition (psychology)

Mathematics

Feature (linguistics)

Artificial intelligence

Chemistry

Deep learning

Artificial neural network

Interpretability

Convolutional neural network

Digit recognition

Arithmetic

IEEE Transactions on Neural Networks and Learning Systems

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.

Morphological Convolutional Neural Network Architecture for Digit Recognition