Transfer Learning in Hierarchical Feature Spaces

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
Procedings of the 10th International Conference on Intelligent Systems and Knowledge Engineering, 2015, pp. 183 - 188
Issue Date:
2015
Full metadata record
Files in This Item:
Filename Description Size
Zuo-ISKE-2015.pdfAccepted Manuscript version435.18 kB
Adobe PDF
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
Please use this identifier to cite or link to this item: