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Semi-Supervised Metric Learning: A Deep Resurrection
In this paper, we address the problem of Semi-Supervised DML (SSDML) that tries to learn a metric using a few labeled examples, and abundantly available unlabeled examples.
Unsupervised Deep Metric Learning via Orthogonality based Probabilistic Loss
As obtaining class labels in all applications is not feasible, we propose an unsupervised approach that learns a metric without making use of class labels.
Affinity guided Geometric Semi-Supervised Metric Learning
In this paper, we revamp the forgotten classical Semi-Supervised Distance Metric Learning (SSDML) problem from a Riemannian geometric lens, to leverage stochastic optimization within a end-to-end deep framework.
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