Deep Metric Learning via Facility Location
Name
1612.01213.pdf
Description
Accepted version
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5.53 MB
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Unknown
Checksum (MD5)
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Author(s)
Song, Hyun Oh
Jegelka, Stefanie Sabrina
Rathod, Vivek
Murphy, Kevin
Date Issued
July 2017
Publisher
IEEE
Citation
Song, Hyun Oh, Jegelka, Stefanie, Rathod, Vivek and Murphy, Kevin. 2017. "Deep Metric Learning via Facility Location."
Version
Author's final manuscript
Abstract
© 2017 IEEE. Learning image similarity metrics in an end-to-end fashion with deep networks has demonstrated excellent results on tasks such as clustering and retrieval. However, current methods, all focus on a very local view of the data. In this paper, we propose a new metric learning scheme, based on structured prediction, that is aware of the global structure of the embedding space, and which is designed to optimize a clustering quality metric (NMI). We show state of the art performance on standard datasets, such as CUB200-2011 [37], Cars196 [18], and Stanford online products [30] on NMI and R@K evaluation metrics.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/cvpr.2017.237
