Transfer learning in hierarchical feature spaces

Publication Type:
Conference Proceeding
Citation:
Proceedings - The 2015 10th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2015, 2016, pp. 183 - 188
Issue Date:
2016-01-13
Metrics:
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© 2015 IEEE. Transfer learning provides an approach to solve target tasks more quickly and effectively by using previously acquired knowledge learned from source tasks. As one category of transfer learning approaches, feature-based transfer learning approaches aim to find a latent feature space shared between source and target domains. The issue is that the sole feature space can't exploit the relationship of source domain and target domain fully. To deal with this issue, this paper proposes a transfer learning method that uses deep learning to extract hierarchical feature spaces, so knowledge of source domain can be exploited and transferred in multiple feature spaces with different levels of abstraction. In the experiment, the effectiveness of transfer learning in multiple feature spaces is compared and this can help us find the optimal feature space for transfer learning.
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