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The structure transfer machine theory and applications
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Zhang, Baochang, Yang, Wankou, Wang, Ze, Zhuo, Lian, Zhen, Xiantong and Han, Jungong (2019) The structure transfer machine theory and applications. IEEE Transactions on Image Processing, 29 . 2889 -2902. doi:10.1109/TIP.2019.2954178 ISSN 1057-7149.
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Official URL: https://doi.org/10.1109/TIP.2019.2954178
Abstract
Representation learning is a fundamental but challenging problem, especially when the distribution of data is unknown. In this paper, we propose a new representation learning method, named Structure Transfer Machine (STM), which enables feature learning process to converge at the representation expectation in a probabilistic way. We theoretically show that such an expected value of the representation (mean) is achievable if the manifold structure can be transferred from the data space to the feature space. The resulting structure regularization term, named manifold loss, is incorporated into the loss function of the typical deep learning pipeline. The STM architecture is constructed to enforce the learned deep representation to satisfy the intrinsic manifold structure from the data, which results in robust features that suit various application scenarios, such as digit recognition, image classification and object tracking. Compared with state-of-the-art CNN architectures, we achieve better results on several commonly used public benchmarks.
Item Type: | Journal Article | |||||||||||||||||||||
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Subjects: | Q Science > QA Mathematics | |||||||||||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | |||||||||||||||||||||
Library of Congress Subject Headings (LCSH): | Machine theory, Neural networks (Computer science) | |||||||||||||||||||||
Journal or Publication Title: | IEEE Transactions on Image Processing | |||||||||||||||||||||
Publisher: | IEEE | |||||||||||||||||||||
ISSN: | 1057-7149 | |||||||||||||||||||||
Official Date: | 25 November 2019 | |||||||||||||||||||||
Dates: |
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Volume: | 29 | |||||||||||||||||||||
Page Range: | 2889 -2902 | |||||||||||||||||||||
DOI: | 10.1109/TIP.2019.2954178 | |||||||||||||||||||||
Status: | Peer Reviewed | |||||||||||||||||||||
Publication Status: | Published | |||||||||||||||||||||
Reuse Statement (publisher, data, author rights): | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||||||||
Date of first compliant deposit: | 22 November 2019 | |||||||||||||||||||||
Date of first compliant Open Access: | 25 November 2019 | |||||||||||||||||||||
RIOXX Funder/Project Grant: |
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