Abstract
Genetic-algorithm-based Local Binary Convolutional Neural Network for Gender Recognition
Highlights
Owing to the substantial increases in the power of computer hardware equipment and computing performance, artificial intelligence applications have flourished
The major contributions of this study are as follows: 1. The proposed genetic algorithm (GA)-local binary convolutional neural network (LBCNN) solves the problem of fixed filter parameters in the local binary convolution (LBC) layer to improve accuracy
Many state-of-art networks have proved that increasing the number of layers of the convolutional neural networks (CNNs) is a good way to improve accuracy; it introduces problems such as increased storage space and computational complexity
Summary
Owing to the substantial increases in the power of computer hardware equipment and computing performance, artificial intelligence applications have flourished. Proposed LeNet,(1) many convolutional neural network (CNN) models have appeared one after another, such as AlexNet,(2) VGGNet,(3) GoogLeNet,(4) ResNet, and DenseNet.[5,6] Both AlexNet and VGGNet improve accuracy by increasing the number of layers. CNNs can only be used with high-performance equipment To resolve these problems, we improve the standard CNN model and reduce the numbers of parameters and calculations to achieve higher accuracy in this study. Backward approximation was applied to manage the gradient mismatch problem in backward propagation They concluded that both the compression and acceleration abilities are guaranteed by utilizing intermediate integers in quantization; the method reaches state-of-the-art performance and can be flexibly used on various networks and with different datasets. The proposed GA-LBCNN solves the problem of fixed filter parameters in the LBC layer to improve accuracy.
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