  Model compression aims to reduce the redundancy of deep networks to obtain
compact models. Recently, channel pruning has become one of the predominant
compression methods to deploy deep models on resource-constrained devices. Most
channel pruning methods often use a fixed compression rate for all the layers
of the model, which, however, may not be optimal. To address this issue, given
a target compression rate for the whole model, one can search for the optimal
compression rate for each layer. Nevertheless, these methods perform channel
pruning for a specific target compression rate. When we consider multiple
compression rates, they have to repeat the channel pruning process multiple
times, which is very inefficient yet unnecessary. To address this issue, we
propose a Conditional Automated Channel Pruning(CACP) method to obtain the
compressed models with different compression rates through single channel
pruning process. To this end, we develop a conditional model that takes an
arbitrary compression rate as input and outputs the corresponding compressed
model. In the experiments, the resultant models with different compression
rates consistently outperform the models compressed by existing methods with a
channel pruning process for each target compression rate.
