Deep Metric Learning via Facility Location

Hyun Oh Song; Stefanie Jegelka; Vivek Rathod; Kevin Murphy

CVPR 2017spotlight365 citations

Deep Metric Learning via Facility Location

Hyun Oh Song, Stefanie Jegelka, Vivek Rathod, Kevin Murphy

Abstract

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, Cars196, and Stanford online products on NMI and R@K evaluation metrics.

BibTeX

@inproceedings{cvpr2017_deepmetriclearni,
  title = {Deep Metric Learning via Facility Location},
  author = {Hyun Oh Song and Stefanie Jegelka and Vivek Rathod and Kevin Murphy},
  booktitle = {CVPR 2017},
  year = {2017}
}