Heterogeneous Double-Head Ensemble for Deep Metric Learning

Youngmin Ro, Jin Young Choi

Research output: Contribution to journalArticlepeer-review


The structure of a multi-head ensemble has been employed by many algorithms in various applications including deep metric learning. However, their structures have been empirically designed in a simple way such as using the same head structure, which leads to a limited ensemble effect due to lack of head diversity. In this paper, for an elaborate design of the multi-head ensemble structure, we establish design concepts based on three structural factors: designing the feature layer for extracting the ensemble-favorable feature vector, designing the shared part for memory savings, and designing the diverse multi-heads for performance improvement. Through rigorous evaluation of variants on the basis of the design concepts, we propose a heterogeneous double-head ensemble structure that drastically increases ensemble gain along with memory savings. In verifying experiments on image retrieval datasets, the proposed ensemble structure outperforms the state-of-the-art algorithms by margins of over 5.3%, 6.1%, 5.9%, and 1.8% in CUB-200, Car-196, SOP, and Inshop, respectively.

Original languageEnglish
Article number9123761
Pages (from-to)118525-118533
Number of pages9
JournalIEEE Access
StatePublished - 2020


  • Ensemble learning
  • deep architecture design
  • deep metric learning
  • image retrieval
  • multi-head structure


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