Modular learning of convolutional neural networks
In recent years, a wide variety of network structures and training methods have been proposed for deep learning. However, the underlying mechanism of a deep network is not fully understood, which is considered as a black box system. The layer-wise learning method was proposed a decade ago, but now i...
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2023
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sg-ntu-dr.10356-1639992023-01-03T05:47:01Z Modular learning of convolutional neural networks Wang, Jinhua Cheah Chien Chern School of Electrical and Electronic Engineering ECCCheah@ntu.edu.sg Engineering::Electrical and electronic engineering Engineering::Computer science and engineering In recent years, a wide variety of network structures and training methods have been proposed for deep learning. However, the underlying mechanism of a deep network is not fully understood, which is considered as a black box system. The layer-wise learning method was proposed a decade ago, but now it is rarely used due to the trade-off in performance as compared to the standard end-to-end learning. Recently, layer-wise learning has been considered for application in interpretable or analytical neural networks. Therefore, a key target is to improve the performance of the layer-wise learning. In this dissertation, a modular deep learning method is developed on the basis of classical layer-wise learning. In addition, the network performance is further improved by proposing epoch-wise learning on the basis of modular deep learning. Through the case studies using several common datasets, the proposed approaches are compared with the traditional layer-wise learning method in terms of performance. Master of Science (Computer Control and Automation) 2023-01-03T05:47:01Z 2023-01-03T05:47:01Z 2022 Thesis-Master by Coursework Wang, J. (2022). Modular learning of convolutional neural networks. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/163999 https://hdl.handle.net/10356/163999 en ISM-DISS-03097 application/pdf Nanyang Technological University |
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Engineering::Electrical and electronic engineering Engineering::Computer science and engineering Wang, Jinhua Modular learning of convolutional neural networks |
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In recent years, a wide variety of network structures and training methods have been proposed for deep learning. However, the underlying mechanism of a deep network is not fully understood, which is considered as a black box system. The layer-wise learning method was proposed a decade ago, but now it is rarely used due to the trade-off in performance as compared to the standard end-to-end learning. Recently, layer-wise learning has been considered for application in interpretable or analytical neural networks. Therefore, a key target is to improve the performance of the layer-wise learning.
In this dissertation, a modular deep learning method is developed on the basis of classical layer-wise learning. In addition, the network performance is further improved by proposing epoch-wise learning on the basis of modular deep learning. Through the case studies using several common datasets, the proposed approaches are compared with the traditional layer-wise learning method in terms of performance. |
author2 |
Cheah Chien Chern |
author_facet |
Cheah Chien Chern Wang, Jinhua |
format |
Thesis-Master by Coursework |
author |
Wang, Jinhua |
author_sort |
Wang, Jinhua |
title |
Modular learning of convolutional neural networks |
title_short |
Modular learning of convolutional neural networks |
title_full |
Modular learning of convolutional neural networks |
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Modular learning of convolutional neural networks |
title_full_unstemmed |
Modular learning of convolutional neural networks |
title_sort |
modular learning of convolutional neural networks |
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Nanyang Technological University |
publishDate |
2023 |
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https://hdl.handle.net/10356/163999 |
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1754611258220347392 |