  Distance Metric Learning (DML) seeks to learn a discriminative embedding
where similar examples are closer, and dissimilar examples are apart. 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. SSDML is important because it is infeasible to manually annotate all
the examples present in a large dataset. Surprisingly, with the exception of a
few classical approaches that learn a linear Mahalanobis metric, SSDML has not
been studied in the recent years, and lacks approaches in the deep SSDML
scenario. In this paper, we address this challenging problem, and revamp SSDML
with respect to deep learning. In particular, we propose a stochastic,
graph-based approach that first propagates the affinities between the pairs of
examples from labeled data, to that of the unlabeled pairs. The propagated
affinities are used to mine triplet based constraints for metric learning. We
impose orthogonality constraint on the metric parameters, as it leads to a
better performance by avoiding a model collapse.
