TELKOMNIKA Telecommunication, Computing, Electronics and Control
Half Gaussian-based wavelet transform for pooling layer for convolution neural network

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Half Gaussian-based wavelet transform for pooling layer for convolution neural network

Convolution neural network
Gaussian
HGT1
HGT2
HGT3

Pooling methods are used to select most significant features to be aggregated
to small region. In this paper, anew pooling method is proposed based on
probability function. Depending on the fact that, most information is
concentrated from mean of the signal to its maximum values, upper half of
Gaussian function is used to determine weights of the basic signal statistics,
which is used to determine the transform of the original signal into more
concise formula, which can represent signal features, this method named half
Gaussian transform (HGT). Based on strategy of transform computation,
Three methods are proposed, the first method (HGT1) is used basic statistics
after normalized it as weights to be multiplied by original signal, second
method (HGT2) is used determined statistics as features of the original signal
and multiply it with constant weights based on half Gaussian, while the third
method (HGT3) is worked in similar to (HGT1) except, it depend on entire
signal. The proposed methods are applied on three databases, which are
(MNIST, CIFAR10 and MIT-BIH ECG) database. The experimental results
show that, our methods are achieved good improvement, which is
outperformed standard pooling methods such as max pooling and average
pooling.

Aqeel M. Hamad Alhussainy, Ammar D. Jasim

http://journal.uad.ac.id/index.php/TELKOMNIKA

Aug 29, 2020

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