Adaptive hybrid activation function for deep neural networks

Published in

(2022)

External Links:

TL;DR

The adaptive hybrid activation function (AHAF) is proposed that combines the properties of the rectifier units and the squashing functions and provides high performance for the neural network training process.

Abstract

The adaptive hybrid activation function (AHAF) is proposed that combines the properties of the rectifier units and the squashing functions. The proposed function can be used as a drop-in replacement for ReLU, SiL and Swish activations for deep neural networks and can evolve to one of such functions during the training. The effectiveness of the function was evaluated on the image classification task using the Fashion-MNIST and CIFAR-10 datasets. The evaluation shows that the neural networks with AHAF activations achieve better classification accuracy comparing to their base implementations that use ReLU and SiL. A double-stage parameter tuning process for training the neural networks with AHAF is proposed. The proposed approach is sufficiently simple from the implementation standpoint and provides high performance for the neural network training process.

Authors

Y. Bodyanskiy

1 papers

Serhii Kostiuk

2 papers

References33 items

Universal activation function for machine learning

PyTorch: An Imperative Style, High-Performance Deep Learning Library

Weighted Sigmoid Gate Unit for an Activation Function of Deep Neural Network

Adaptive Blending Units: Trainable Activation Functions for Deep Neural Networks

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Adaptive hybrid activation function for deep neural networks

Published in

(2022)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

Authors

Y. Bodyanskiy

1 papers

Serhii Kostiuk

2 papers

TL;DR

Abstract

Authors

References33 items

Universal activation function for machine learning

PyTorch: An Imperative Style, High-Performance Deep Learning Library

Weighted Sigmoid Gate Unit for an Activation Function of Deep Neural Network

Adaptive Blending Units: Trainable Activation Functions for Deep Neural Networks

Learning Combinations of Activation Functions

TL;DR

Abstract

Authors

References33 items

Universal activation function for machine learning

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Field of Study

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