Nanni, L.; Lumini, A.; Ghidoni, S.; Maguolo, G. Stochastic Selection of Activation Layers for Convolutional Neural Networks. Sensors2020, 20, 1626.
Nanni, L.; Lumini, A.; Ghidoni, S.; Maguolo, G. Stochastic Selection of Activation Layers for Convolutional Neural Networks. Sensors 2020, 20, 1626.
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.
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.