Image recognition based on deep residual shrinkage Network

Deep residual shrinkage network is a novel and improved algorithm based on deep residual shrinkage network by introducing attention mechanism and soft threshold function. The core of the deep residual shrinkage network can be expressed as: when processing the image sample data, the important informa...

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Bibliographic Details
Published in2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) pp. 334 - 337
Main Authors Li, Yu, Chen, Hongguan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2021
Subjects
attention mechanism
component
Data models
Deep learning
deep residual shrinkage network
Feature extraction
Image recognition
soft threshold function
Supervised learning
Training
Transfer learning
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Summary:Deep residual shrinkage network is a novel and improved algorithm based on deep residual shrinkage network by introducing attention mechanism and soft threshold function. The core of the deep residual shrinkage network can be expressed as: when processing the image sample data, the important information features are extracted by the attention mechanism, and then the negligible information features are eliminated by the soft threshold function, only important information is retained, and these important information features are processed to get the final results, so as to improve the accuracy. The deep residual shrinkage network was applied to MNIST dataset and CIFAR-10 dataset to determine the feasibility of this method in image recognition, and then the network was applied to the facial expression dataset made by the author based on FER2013. Experimental results show that this method can effectively improve the accuracy of image recognition.
DOI:10.1109/AIEA53260.2021.00077