Please use this identifier to cite or link to this item:
Title: Deep transfer metric learning
Authors: Hu, Junlin
Lu, Jiwen
Tan, Yap Peng
Keywords: Face
Face recognition
Learning systems
Machine learning
Issue Date: 2015
Source: Hu, J., Lu, J., & Tan, Y.-P. (2015). Deep transfer metric learning. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 325-333.
Conference: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Abstract: Conventional metric learning methods usually assume that the training and test samples are captured in similar scenarios so that their distributions are assumed to be the same. This assumption doesn't hold in many real visual recognition applications, especially when samples are captured across different datasets. In this paper, we propose a new deep transfer metric learning (DTML) method to learn a set of hierarchical nonlinear transformations for cross-domain visual recognition by transferring discriminative knowledge from the labeled source domain to the unlabeled target domain. Specifically, our DTML learns a deep metric network by maximizing the inter-class variations and minimizing the intra-class variations, and minimizing the distribution divergence between the source domain and the target domain at the top layer of the network. To better exploit the discriminative information from the source domain, we further develop a deeply supervised transfer metric learning (DSTML) method by including an additional objective on DTML where the output of both the hidden layers and the top layer are optimized jointly. Experimental results on cross-dataset face verification and person re-identification validate the effectiveness of the proposed methods.
DOI: 10.1109/CVPR.2015.7298629
Schools: School of Electrical and Electronic Engineering 
Rights: © 2015 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. The published version is available at: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

Files in This Item:
File Description SizeFormat 
Deep transfer metric learning.pdf1.08 MBAdobe PDFThumbnail

Citations 5

Updated on Jun 2, 2024

Page view(s) 10

Updated on Jun 16, 2024

Download(s) 10

Updated on Jun 16, 2024

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.