  In this paper, we explore the redundancy in convolutional neural network,
which scales with the complexity of vision tasks. Considering that many
front-end visual systems are interested in only a limited range of visual
targets, the removing of task-specified network redundancy can promote a wide
range of potential applications. We propose a task-specified knowledge
distillation algorithm to derive a simplified model with pre-set computation
cost and minimized accuracy loss, which suits the resource constraint front-end
systems well. Experiments on the MNIST and CIFAR10 datasets demonstrate the
feasibility of the proposed approach as well as the existence of task-specified
redundancy.
