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. With a single PruningNet trained for the target network, we can search for various Pruned Networks under different constraints with little human participation. We have demonstrated competitive performances on MobileNet V1/V2 networks, up to 9.0/9.9 higher ImageNet accuracy than V1/V2. Compared to the previous state-of-the-art AutoML-based pruning methods, like AMC and NetAdapt, we achieve higher or comparable accuracy under various conditions.

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。

MetaPruning：用于自动神经网络通道剪枝的元学习