  Distillation-based learning boosts the performance of the miniaturized neural
network based on the hypothesis that the representation of a teacher model can
be used as structured and relatively weak supervision, and thus would be easily
learned by a miniaturized model. However, we find that the representation of a
converged heavy model is still a strong constraint for training a small student
model, which leads to a high lower bound of congruence loss. In this work,
inspired by curriculum learning we consider the knowledge distillation from the
perspective of curriculum learning by routing. Instead of supervising the
student model with a converged teacher model, we supervised it with some anchor
points selected from the route in parameter space that the teacher model passed
by, as we called route constrained optimization (RCO). We experimentally
demonstrate this simple operation greatly reduces the lower bound of congruence
loss for knowledge distillation, hint and mimicking learning. On close-set
classification tasks like CIFAR100 and ImageNet, RCO improves knowledge
distillation by 2.14% and 1.5% respectively. For the sake of evaluating the
generalization, we also test RCO on the open-set face recognition task
MegaFace.
