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Open AccessArticle

Stochastic Selection of Activation Layers for Convolutional Neural Networks

1
Department of Information Enginering, University of Padua, viale Gradenigo 6, 35131 Padua, Italy
2
DISI, Università di Bologna, Via dell’università 50, 47521 Cesena, Italy
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(6), 1626; https://doi.org/10.3390/s20061626
Received: 14 February 2020 / Revised: 11 March 2020 / Accepted: 12 March 2020 / Published: 14 March 2020
(This article belongs to the Special Issue Machine Learning for Biomedical Imaging and Sensing)
In recent years, the field of deep learning has achieved considerable success in pattern recognition, image segmentation, and many other classification fields. There are many studies and practical applications of deep learning on images, video, or text classification. Activation functions play a crucial role in discriminative capabilities of the deep neural networks and the design of new “static” or “dynamic” activation functions is an active area of research. The main difference between “static” and “dynamic” functions is that the first class of activations considers all the neurons and layers as identical, while the second class learns parameters of the activation function independently for each layer or even each neuron. Although the “dynamic” activation functions perform better in some applications, the increased number of trainable parameters requires more computational time and can lead to overfitting. In this work, we propose a mixture of “static” and “dynamic” activation functions, which are stochastically selected at each layer. Our idea for model design is based on a method for changing some layers along the lines of different functional blocks of the best 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. View Full-Text
Keywords: Convolutional Neural Networks; ensemble of classifiers; activation functions; image classification; skin detection Convolutional Neural Networks; ensemble of classifiers; activation functions; image classification; skin detection
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Nanni, L.; Lumini, A.; Ghidoni, S.; Maguolo, G. Stochastic Selection of Activation Layers for Convolutional Neural Networks. Sensors 2020, 20, 1626.

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