  Visual Similarity plays an important role in many computer vision
applications. Deep metric learning (DML) is a powerful framework for learning
such similarities which not only generalize from training data to identically
distributed test distributions, but in particular also translate to unknown
test classes. However, its prevailing learning paradigm is class-discriminative
supervised training, which typically results in representations specialized in
separating training classes. For effective generalization, however, such an
image representation needs to capture a diverse range of data characteristics.
To this end, we propose and study multiple complementary learning tasks,
targeting conceptually different data relationships by only resorting to the
available training samples and labels of a standard DML setting. Through
simultaneous optimization of our tasks we learn a single model to aggregate
their training signals, resulting in strong generalization and state-of-the-art
performance on multiple established DML benchmark datasets.
