Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

Asymmetric Metric Learning for Knowledge Transfer

Mateusz Budnik Yannis Avrithis 1
1 LinkMedia - Creating and exploiting explicit links between multimedia fragments
Inria Rennes – Bretagne Atlantique , IRISA-D6 - MEDIA ET INTERACTIONS
Abstract : Knowledge transfer from large teacher models to smaller student models has recently been studied for metric learning, focusing on fine-grained classification. In this work, focusing on instance-level image retrieval, we study an asymmetric testing task, where the database is represented by the teacher and queries by the student. Inspired by this task, we introduce asymmetric metric learning, a novel paradigm of using asymmetric representations at training. This acts as a simple combination of knowledge transfer with the original metric learning task. We systematically evaluate different teacher and student models, metric learning and knowledge transfer loss functions on the new asymmetric testing as well as the standard symmetric testing task, where database and queries are represented by the same model. We find that plain regression is surprisingly effective compared to more complex knowledge transfer mechanisms, working best in asymmetric testing. Interestingly, our asymmetric metric learning approach works best in symmetric testing, allowing the student to even outperform the teacher.
Document type :
Preprints, Working Papers, ...
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03047591
Contributor : Yannis Avrithis Connect in order to contact the contributor
Submitted on : Tuesday, December 8, 2020 - 9:44:20 PM
Last modification on : Wednesday, November 3, 2021 - 8:15:37 AM
Long-term archiving on: : Tuesday, March 9, 2021 - 8:15:51 PM

Identifiers

  • HAL Id : hal-03047591, version 1
  • ARXIV : 2006.16331

Citation

Mateusz Budnik, Yannis Avrithis. Asymmetric Metric Learning for Knowledge Transfer. 2020. ⟨hal-03047591⟩

Share

Metrics

Record views

39

Files downloads

99