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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| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/163999 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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. |
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