Abstract
In this paper, we introduce a new regularization technique for transfer learning. The aim of the proposed approach is to capture statistical relationships among convolution filters learned from a well-Trained network and transfer this knowledge to another network. Since convolution filters of the prevalent deep Convolutional Neural Network (CNN) models share a number of similar patterns, in order to speed up the learning procedure, we capture such correlations by Gaussian Mixture Models (GMMs) and transfer them using a regularization term. We have conducted extensive experiments on the CIFAR10, Places2, and CM-Places datasets to assess generalizability, task transferability, and cross-model transferability of the proposed approach, respectively. The experimental results show that the feature representations have efficiently been learned and transferred through the proposed statistical regularization scheme. Moreover, our method is an architecture independent approach, which is applicable for a variety of CNN architectures.
Original language | English |
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Title of host publication | Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 2674-2680 |
Number of pages | 7 |
ISBN (Electronic) | 9781538610343 |
DOIs | |
Publication status | Published - 1 Jul 2017 |
Event | 16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017 - Venice, Italy Duration: 22 Oct 2017 → 29 Oct 2017 |
Publication series
Name | Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017 |
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Volume | 2018-January |
Conference
Conference | 16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017 |
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Country/Territory | Italy |
City | Venice |
Period | 22/10/17 → 29/10/17 |
Bibliographical note
Publisher Copyright:© 2017 IEEE.