Novel Adaptive Binary Search Strategy-First Hybrid Pyramid- and Clustering-Based CNN Filter Pruning Method without Parameters Setting
This work addresses the challenge of reducing computational costs in CNN models for applications like image processing, but it appears incremental as it builds on existing pruning techniques with a novel hybrid approach.
The paper tackles the problem of pruning redundant filters in CNN models by proposing an adaptive binary search-first hybrid pyramid- and clustering-based method that automatically removes filters without parameter setting, resulting in higher accuracy and significant reductions in parameters and floating-point operations compared to state-of-the-art methods.
Pruning redundant filters in CNN models has received growing attention. In this paper, we propose an adaptive binary search-first hybrid pyramid- and clustering-based (ABSHPC-based) method for pruning filters automatically. In our method, for each convolutional layer, initially a hybrid pyramid data structure is constructed to store the hierarchical information of each filter. Given a tolerant accuracy loss, without parameters setting, we begin from the last convolutional layer to the first layer; for each considered layer with less or equal pruning rate relative to its previous layer, our ABSHPC-based process is applied to optimally partition all filters to clusters, where each cluster is thus represented by the filter with the median root mean of the hybrid pyramid, leading to maximal removal of redundant filters. Based on the practical dataset and the CNN models, with higher accuracy, the thorough experimental results demonstrated the significant parameters and floating-point operations reduction merits of the proposed filter pruning method relative to the state-of-the-art methods.