Filter pruning is a common method to achieve model compression and
acceleration in deep neural networks (DNNs).Some research regarded filter
pruning as a combinatorial optimization problem and thus used evolutionary
algorithms (EA) to prune filters of DNNs. However, it is difficult to find a
satisfactory compromise solution in a reasonable time due to the complexity of
solution space searching. To solve this problem, we first formulate a
multi-objective optimization problem based on a sub-network of the full model
and propose a Sub-network Multiobjective Evolutionary Algorithm (SMOEA) for
filter pruning. By progressively pruning the convolutional layers in groups,
SMOEA can obtain a lightweight pruned result with better
performance.Experiments on VGG-14 model for CIFAR-10 verify the effectiveness
of the proposed SMOEA. Specifically, the accuracy of the pruned model with
16.56% parameters decreases by 0.28% only, which is better than the widely used
popular filter pruning criteria.

本研究提出了一种基于子网络的多目标进化算法用于卷积神经网络过滤器裁剪，通过逐步组合卷积层裁剪，可以在保持更好性能的同时得到轻量化的裁剪结果。