  Knowledge transfer from large teacher models to smaller student models has
recently been studied for metric learning, focusing on fine-grained
classification. In this work, focusing on instance-level image retrieval, we
study an asymmetric testing task, where the database is represented by the
teacher and queries by the student. Inspired by this task, we introduce
asymmetric metric learning, a novel paradigm of using asymmetric
representations at training. This acts as a simple combination of knowledge
transfer with the original metric learning task.
  We systematically evaluate different teacher and student models, metric
learning and knowledge transfer loss functions on the new asymmetric testing as
well as the standard symmetric testing task, where database and queries are
represented by the same model. We find that plain regression is surprisingly
effective compared to more complex knowledge transfer mechanisms, working best
in asymmetric testing. Interestingly, our asymmetric metric learning approach
works best in symmetric testing, allowing the student to even outperform the
teacher.
