  Deep metric learning is often used to learn an embedding function that
captures the semantic differences within a dataset. A key factor in many
problem domains is how this embedding generalizes to new classes of data. In
observing many triplet selection strategies for Metric Learning, we find that
the best performance consistently arises from approaches that focus on a few,
well selected triplets.We introduce visualization tools to illustrate how an
embedding generalizes beyond measuring accuracy on validation data, and we
illustrate the behavior of a range of triplet selection strategies.
