  Learning a model of perceptual similarity from a collection of objects is a
fundamental task in machine learning underlying numerous applications. A common
way to learn such a model is from relative comparisons in the form of triplets:
responses to queries of the form "Is object a more similar to b than it is to
c?". If no consideration is made in the determination of which queries to ask,
existing similarity learning methods can require a prohibitively large number
of responses. In this work, we consider the problem of actively learning from
triplets -finding which queries are most useful for learning. Different from
previous active triplet learning approaches, we incorporate auxiliary
information into our similarity model and introduce an active learning scheme
to find queries that are informative for quickly learning both the relevant
aspects of auxiliary data and the directly-learned similarity components.
Compared to prior approaches, we show that we can learn just as effectively
with much fewer queries. For evaluation, we introduce a new dataset of
exhaustive triplet comparisons obtained from humans and demonstrate improved
performance for different types of auxiliary information.
