ARTICLE | doi:10.20944/preprints202002.0231.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: Convolutional Neural Networks; ensemble of classifiers; activation functions; image classification; skin detection
Online: 17 February 2020 (01:50:08 CET)
In recent years, the field of deep learning achieved considerable success in pattern recognition, image segmentation and may other classification fields. There are a lot of studies and practical applications of deep learning on images, video or text classification. In this study, we suggest a method for changing the architecture of the most performing CNN models with the aim of designing new models to be used as stand-alone networks or as a component of an ensemble. We propose to replace each activation layer of a CNN (usually a ReLu layer) by a different activation function stochastically drawn from a set of activation functions: in this way the resulting CNN has a different set of activation function layers.