A Metric Learning Reality Check

August 18, 2020

Abstract

Abstract. Deep metric learning papers from the past four years have consistently claimed great advances in accuracy, often more than doubling the performance of decade-old methods. In this paper, we take a closer look at the field to see if this is actually true. We find flaws in the experimental methodology of numerous metric learning papers, and show that the actual improvements over time have been marginal at best. Code is available at github.com/KevinMusgrave/powerful-benchmarker.

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AUTHORS

Written by

Ser-Nam Lim

Publisher

ECCV

Research Topics

Computer Vision

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