  Good quality similarity metrics can significantly facilitate the performance
of many large-scale, real-world applications. Existing studies have proposed
various solutions to learn a Mahalanobis or bilinear metric in an online
fashion by either restricting distances between similar (dissimilar) pairs to
be smaller (larger) than a given lower (upper) bound or requiring similar
instances to be separated from dissimilar instances with a given margin.
However, these linear metrics learned by leveraging fixed bounds or margins may
not perform well in real-world applications, especially when data distributions
are complex. We aim to address the open challenge of "Online Adaptive Metric
Learning" (OAML) for learning adaptive metric functions on the fly. Unlike
traditional online metric learning methods, OAML is significantly more
challenging since the learned metric could be non-linear and the model has to
be self-adaptive as more instances are observed. In this paper, we present a
new online metric learning framework that attempts to tackle the challenge by
learning an ANN-based metric with adaptive model complexity from a stream of
constraints. In particular, we propose a novel Adaptive-Bound Triplet Loss
(ABTL) to effectively utilize the input constraints and present a novel
Adaptive Hedge Update (AHU) method for online updating the model parameters. We
empirically validate the effectiveness and efficacy of our framework on various
applications such as real-world image classification, facial verification, and
image retrieval.
