Transfer learning algorithm for image classification task and its convergence analysis
Theoretical analysis of transfer learning of the deep neural networks (DNN) is crucial in ensuring stability or convergence and gaining a better understanding of the networks for further development. However, most current transfer learning methods are black-box approaches that are more focused...
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| Main Authors: | , |
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| Other Authors: | |
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/172282 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Theoretical analysis of transfer learning of the
deep neural networks (DNN) is crucial in ensuring stability or
convergence and gaining a better understanding of the networks
for further development. However, most current transfer learning
methods are black-box approaches that are more focused
on empirical studies. This paper develops a transfer learning
algorithm for deep convolutional neural networks (CNN) with
batch normalization layers. A convergence-guaranteed transfer
learning algorithm is proposed to train the classifier of a deep
CNN with pretrained convolutional layers. Two classification case
studies based on VGG11 with the MNIST dataset and CIFAR10
dataset are presented to demonstrate the performance of the
proposed approach and explore the effect of batch normalization
layers on transfer learning. |
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