  Deep neural networks often have a huge number of parameters, which posts
challenges in deployment in application scenarios with limited memory and
computation capacity. Knowledge distillation is one approach to derive compact
models from bigger ones. However, it has been observed that a converged heavy
teacher model is strongly constrained for learning a compact student network
and could make the optimization subject to poor local optima. In this paper, we
propose ProKT, a new model-agnostic method by projecting the supervision
signals of a teacher model into the student's parameter space. Such projection
is implemented by decomposing the training objective into local intermediate
targets with an approximate mirror descent technique. The proposed method could
be less sensitive with the quirks during optimization which could result in a
better local optimum. Experiments on both image and text datasets show that our
proposed ProKT consistently achieves superior performance compared to other
existing knowledge distillation methods.
