Learning Deep Morphological Networks with Neural Architecture Search

• URL: http://arxiv.org/abs/2106.07714v1
• Date: Mon, 14 Jun 2021 19:19:48 GMT
• Title: Learning Deep Morphological Networks with Neural Architecture Search
• Authors: Yufei Hu, Nacim Belkhir, Jesus Angulo, Angela Yao, Gianni Franchi
• Abstract summary: We propose a method based on meta-learning to incorporate morphological operators into Deep Neural Networks. The learned architecture demonstrates how our novel morphological operations significantly increase DNN performance on various tasks.
• Score: 19.731352645511052
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep Neural Networks (DNNs) are generated by sequentially performing linear and non-linear processes. Using a combination of linear and non-linear procedures is critical for generating a sufficiently deep feature space. The majority of non-linear operators are derivations of activation functions or pooling functions. Mathematical morphology is a branch of mathematics that provides non-linear operators for a variety of image processing problems. We investigate the utility of integrating these operations in an end-to-end deep learning framework in this paper. 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 and edge detection.

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