Paper

Affinity guided Geometric Semi-Supervised Metric Learning

In this paper, we address the semi-supervised metric learning problem, where we learn a distance metric using very few labeled examples, and additionally available unlabeled data. To address the limitations of existing semi-supervised approaches, we integrate some of the best practices across metric learning, to achieve the state-of-the-art in the semi-supervised setting... (read more)

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