Model compression plays a vital role in the practical deployment of deep neural networks (DNNs), and evolutionary multi-objective (EMO) pruning is an essential tool in balancing the compression rate and performance of the DNNs. However, due to its population-based nature, EMO pruning suffers from the complex optimization space and the resource-intensive structure verification process, especially in complex networks. To this end, a multi-objective complex network pruning framework based on divide-and-conquer and global performance impairment ranking (EMO-DIR) is proposed in this paper. Firstly, a divide-and-conquer EMO network pruning method is proposed, which decomposes the complex task of EMO pruning on the entire network into easier sub-tasks on multiple sub-networks. On the one hand, this decomposition narrows the pruning optimization space and decreases the optimization difficulty; on the other hand, the smaller network structure converges faster, so the proposed algorithm consumes lower computational resources. Secondly, a sub-network training method based on cross-network constraints is designed, which could bridge independent EMO pruning sub-tasks, allowing them to collaborate better and improving the overall performance of the pruned network. Finally, a multiple sub-networks joint pruning method based on EMO is proposed. This method combines the Pareto Fronts from EMO pruning results on multiple sub-networks through global performance impairment ranking to design a joint pruning scheme. The rich experiments on CIFAR-10/100 and ImageNet-100/1k are conducted. The proposed algorithm achieves a comparable performance with the state-of-the-art pruning methods.

翻译：模型压缩在深度神经网络的实际部署中扮演着关键角色，而进化多目标剪枝是平衡深度神经网络压缩率与性能的重要工具。然而，受种群优化本质的影响，进化多目标剪枝面临复杂优化空间和资源密集型结构验证过程的双重挑战，尤其在复杂网络中更为突出。为此，本文提出一种基于分治策略与全局性能损害排序的多目标复杂网络剪枝框架。首先，提出分治式进化多目标网络剪枝方法，将全网络层面的复杂剪枝任务分解为多个子网络上的简易子任务。这种分解一方面缩小了剪枝优化空间并降低优化难度，另一方面使更小的网络结构收敛更快，从而降低算法计算资源消耗。其次，设计基于跨网络约束的子网络训练方法，能够连通独立的进化多目标剪枝子任务，促进它们协同合作并提升剪枝网络的整体性能。最后，提出基于进化多目标的多子网络联合剪枝方法，通过全局性能损害排序整合多个子网络进化多目标剪枝结果的帕累托前沿，设计联合剪枝方案。在CIFAR-10/100和ImageNet-100/1k上开展大量实验，所提算法取得了与最先进剪枝方法可比的性能。