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
this https URL

本文提出了一种基于元学习的通道自动剪枝方法。首先使用简单的随机结构采样方法训练一个 PruningNet 元网络，然后应用演化搜索方法寻找良好表现的剪枝网络，其效率高且无需在线微调。实验结果表明，相比现有的剪枝方法，该方法在 MobileNet V1/V2 和 ResNet 等模型上有着更好的性能表现。