  Channel pruning is a promising technique to compress the parameters of deep
convolutional neural networks(DCNN) and to speed up the inference. This paper
aims to address the long-standing inefficiency of channel pruning. Most channel
pruning methods recover the prediction accuracy by re-training the pruned model
from the remaining parameters or random initialization. This re-training
process is heavily dependent on the sufficiency of computational resources,
training data, and human interference(tuning the training strategy). In this
paper, a highly efficient pruning method is proposed to significantly reduce
the cost of pruning DCNN. The main contributions of our method include: 1)
pruning compensation, a fast and data-efficient substitute of re-training to
minimize the post-pruning reconstruction loss of features, 2)
compensation-aware pruning(CaP), a novel pruning algorithm to remove redundant
or less-weighted channels by minimizing the loss of information, and 3) binary
structural search with step constraint to minimize human interference. On
benchmarks including CIFAR-10/100 and ImageNet, our method shows competitive
pruning performance among the state-of-the-art retraining-based pruning methods
and, more importantly, reduces the processing time by 95% and data usage by
90%.
