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Deep Randomized Ensembles for Metric Learning

  • Hong Xuan
  • Richard Souvenir
  • Robert Pless
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11220)

Abstract

Learning embedding functions, which map semantically related inputs to nearby locations in a feature space supports a variety of classification and information retrieval tasks. In this work, we propose a novel, generalizable and fast method to define a family of embedding functions that can be used as an ensemble to give improved results. Each embedding function is learned by randomly bagging the training labels into small subsets. We show experimentally that these embedding ensembles create effective embedding functions. The ensemble output defines a metric space that improves state of the art performance for image retrieval on CUB-200-2011, Cars-196, In-Shop Clothes Retrieval and VehicleID.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceGeorge Washington UniversityWashington, D.C.USA
  2. 2.Department of Computer and Information SciencesTemple UniversityPhiladelphiaUSA

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