Semanlink - [2003.08505] A Metric Learning Reality Check

[2003.08505] A Metric Learning Reality Check

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sl:arxiv_author :
sl:arxiv_firstAuthor : Kevin Musgrave
sl:arxiv_num : 2003.08505
sl:arxiv_published : 2020-03-18T23:28:04Z
sl:arxiv_summary : 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 setup of these papers, and propose a new way to evaluate metric learning algorithms. Finally, we present experimental results that show that the improvements over time have been marginal at best.@en
sl:arxiv_title : A Metric Learning Reality Check@en
sl:arxiv_updated : 2020-03-18T23:28:04Z
sl:bookmarkOf : https://arxiv.org/abs/2003.08505
sl:creationDate : 2020-05-10
sl:creationTime : 2020-05-10T11:06:07Z

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Bookmark of: https://arxiv.org/abs/2003.08505

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> Traditionnaly, supervision is expressed in the form of sets of points that follow
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