Learning deep morphological networks with neural architecture search

Highlights：

• First, we propose novel procedures based on sub-pixel convolutions and mathematical morphology to construct pseudo-morphological operations using standard convolution layers.

• We integrate these procedures into deep networks using morphological layers and NAS algorithms. We demonstrate empirically that our architecture tailored to morphological layers can outperform conventional convolutional layers.

• We outline some current issues in NAS and introduce the problem of choosing the backbone,i.e.the higher-level architecture design on which the search will be performed. We offer novel network space descriptions suitable for the edge identification job.

• We are the first to examine architectural search mixed with morphological procedures for edge detection. Our new specialized architecture achieves state-of-the-art performance for edge detection.