| Autor: |
Gabbasov, Rail, Paringer, Rustam, Kupriyanov, Alexander, Asatryan, David, Haroutunian, Mariam |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2023, Vol. 2757 Issue 1, p1-11, 11p |
| Abstrakt: |
Artificial neural networks are successfully applied to many different problems. Among them, a large class of problems related to computer vision can be distinguished. In this area, the use of convolutional neural networks is particularly successful. Most of the existing neural network architectures are trained on large clusters that require a large amount of computational resources. Therefore, urgent is the task of optimizing neural networks, which can include both increasing performance and reducing the size of the computing power used. In this paper, we propose a method for optimizing (increasing the performance and reducing the amount of consumed resources) of a convolutional neural network, applicable in conditions of redundancy in the input data. Using the Caltech256 dataset and VGG16 network architecture, it was shown that the proposed method can improve network performance by 10% while maintaining accuracy and reducing the amount of resources consumed by 25%. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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