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arXiv · 2207.06413

MorphoActivation: Generalizing ReLU activation function by mathematical morphology

Santiago Velasco-Forero·Jesús Angulo
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Abstract

This paper analyses both nonlinear activation functions and spatial max-pooling for Deep Convolutional Neural Networks (DCNNs) by means of the algebraic basis of mathematical morphology. Additionally, a general family of activation functions is proposed by considering both max-pooling and nonlinear operators in the context of morphological representations. Experimental section validates the goodness of our approach on classical benchmarks for supervised learning by DCNN.

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BibTeXRIS

Santiago Velasco-Forero, Jesús Angulo. 2022-07-13. MorphoActivation: Generalizing ReLU activation function by mathematical morphology. https://arxiv.org/abs/2207.06413

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