  In this paper, we propose a novel meta learning approach for automatic
channel pruning of very deep neural networks. We first train a PruningNet, a
kind of meta network, which is able to generate weight parameters for any
pruned structure given the target network. We use a simple stochastic structure
sampling method for training the PruningNet. Then, we apply an evolutionary
procedure to search for good-performing pruned networks. The search is highly
efficient because the weights are directly generated by the trained PruningNet
and we do not need any finetuning at search time. With a single PruningNet
trained for the target network, we can search for various Pruned Networks under
different constraints with little human participation. Compared to the
state-of-the-art pruning methods, we have demonstrated superior performances on
MobileNet V1/V2 and ResNet. Codes are available on
https://github.com/liuzechun/MetaPruning.
