COP: customized correlation-based Filter level pruning method for deep CNN compression

As deep CNNs get larger, it becomes more challenging to deploy them on resource-restricted mobile devices. Filter-level pruning is one of the most popular methods to compress deep models for mobile deployment. It prunes unimportant filters in the pre-trained CNN to reduce its storage and computational cost, yielding a smaller and more efficient model. Though having made some progress, most filter-level pruning methods still suffer from at least one of the following two problems: 1) High redundancy: some methods pick out unimportant filters without considering their correlations, and thus many highly correlated filters are not pruned, yielding a model still with high redundancy. 2) Sub-optimal: all existing pruning methods achieve sub-optimal pruning plan because they neglect that a high parameters reduction ratio (PRR) does not always mean a high FLOPs reduction ratio (FRR). In this paper, we propose our customized correlation-based pruning (COP) to solve these problems. In particular, we observe redundant filters through their correlations. Moreover, to achieve the optimal pruning plan, PRR and FRR are considered when evaluating filters’ importance. Besides, we also propose a new pruning pipeline, which improves the accuracy of the pruned model. Extensive experiments show that our proposed method has outperformed the state-of-the-art on several popular architectures and datasets.