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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> We address the problem of distance metric learning (DML), defined as learning a distance consistent with a notion of semantic similarity...
> Traditionnaly, supervision is expressed in the form of sets of points that follow
an ordinal relationship – an anchor point x is similar to
a set of positive points Y , and dissimilar to a set of negative
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> Triplet-Based methods are challenging to optimize (a main issue is the need for finding informative triplets).
>
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Mentioned in this [blog post](/doc/2020/01/training_a_speaker_embedding_fr):

> "**Proxy based triplet learning**": instead of generating triplets, we learn an embedding for each class and use the learnt embedding as a proxy for triplets as part of the training. In other words, we can train end to end without the computationally expensive step of resampling triplets after each network update.

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> Our formulation of Proxy-NCA loss produces a loss very
similar to the standard cross-entropy loss used in classification.
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direction: we are not interested in the actual classifier and
indeed discard the proxies once the model has been trained.
Instead, the proxies are auxiliary variables, enabling more
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**As such, our formulation not only enables us to surpass the
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a classifier, and using its penultimate layer’s output as the
embedding.**

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