  Metric learning networks are used to compute image embeddings, which are
widely used in many applications such as image retrieval and face recognition.
In this paper, we propose to use network distillation to efficiently compute
image embeddings with small networks. Network distillation has been
successfully applied to improve image classification, but has hardly been
explored for metric learning. To do so, we propose two new loss functions that
model the communication of a deep teacher network to a small student network.
We evaluate our system in several datasets, including CUB-200-2011, Cars-196,
Stanford Online Products and show that embeddings computed using small student
networks perform significantly better than those computed using standard
networks of similar size. Results on a very compact network (MobileNet-0.25),
which can be used on mobile devices, show that the proposed method can greatly
improve Recall@1 results from 27.5\% to 44.6\%. Furthermore, we investigate
various aspects of distillation for embeddings, including hint and attention
layers, semi-supervised learning and cross quality distillation. (Code is
available at https://github.com/yulu0724/EmbeddingDistillation.)
