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Learning Intra-Batch Connections for Deep Metric Learning
[article]
2021
arXiv
pre-print
The goal of metric learning is to learn a function that maps samples to a lower-dimensional space where similar samples lie closer than dissimilar ones. Particularly, deep metric learning utilizes neural networks to learn such a mapping. Most approaches rely on losses that only take the relations between pairs or triplets of samples into account, which either belong to the same class or two different classes. However, these methods do not explore the embedding space in its entirety. To this
arXiv:2102.07753v3
fatcat:ikholheukvbcxigdxv5cefjkpu