  We have witnessed rapid evolution of deep neural network architecture design
in the past years. These latest progresses greatly facilitate the developments
in various areas such as computer vision and natural language processing.
However, along with the extraordinary performance, these state-of-the-art
models also bring in expensive computational cost. Directly deploying these
models into applications with real-time requirement is still infeasible.
Recently, Hinton etal. have shown that the dark knowledge within a powerful
teacher model can significantly help the training of a smaller and faster
student network. These knowledge are vastly beneficial to improve the
generalization ability of the student model. Inspired by their work, we
introduce a new type of knowledge -- cross sample similarities for model
compression and acceleration. This knowledge can be naturally derived from deep
metric learning model. To transfer them, we bring the "learning to rank"
technique into deep metric learning formulation. We test our proposed DarkRank
method on various metric learning tasks including pedestrian re-identification,
image retrieval and image clustering. The results are quite encouraging. Our
method can improve over the baseline method by a large margin. Moreover, it is
fully compatible with other existing methods. When combined, the performance
can be further boosted.
