"A Theoretically Sound Upper Bound on the Triplet Loss for Improving the ..."

Thanh-Toan Do et al. (2019)

> Home

Details and statistics

DOI: 10.1109/CVPR.2019.01065

access: open

type: Conference or Workshop Paper

metadata version: 2023-08-15

- view
  - electronic edition @ thecvf.com (open access)
  - electronic edition via DOI
  authority control:
- export record
  dblp key:
  - conf/cvpr/DoT0KHC19
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/conf/cvpr/DoT0KHC19
Thanh-Toan Do, Toan Tran, Ian D. Reid, B. G. Vijay Kumar, Tuan Hoang, Gustavo Carneiro:
A Theoretically Sound Upper Bound on the Triplet Loss for Improving the Efficiency of Deep Distance Metric Learning. CVPR 2019: 10404-10413

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.