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DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning
[article]
2020
arXiv
pre-print
Visual Similarity plays an important role in many computer vision applications. Deep metric learning (DML) is a powerful framework for learning such similarities which not only generalize from training data to identically distributed test distributions, but in particular also translate to unknown test classes. However, its prevailing learning paradigm is class-discriminative supervised training, which typically results in representations specialized in separating training classes. For effective
arXiv:2004.13458v4
fatcat:flyvghpkgraivfun5szrcraw7u