Learning deep morphological networks with neural architecture search

Yufei HU, Nacim Belkhir, Jesus Angulo, Angela Yao, Gianni Franchi

Abstract: Deep Neural Networks (DNNs) are generated by sequentially performing linear and non-linear processes. The combination of linear and non-linear procedures is critical for generating a sufficiently deep feature space. Most non-linear operators are derivations of activation functions or pooling functions. Mathematical morphology is a branch of mathematics that provides non-linear operators for various image processing problems. This paper investigates the utility of integrating these operations into an end-to-end deep learning framework. DNNs are designed to acquire a realistic representation for a particular job. Morphological operators give topological descriptors that convey salient information about the shapes of objects depicted in images. We propose a method based on meta-learning to incorporate morphological operators into DNNs. The learned architecture demonstrates how our novel morphological operations significantly increase DNN performance on various tasks, including picture classification, edge detection, and semantic segmentation. Our codes are available at https://nao-morpho.github.io/.