An Effective Backward Filter Pruning Algorithm Using K1,n Bipartite Graph-Based Clustering and the Decreasing Pruning Rate Approach

Setting a fixed pruning rate and/or specified threshold for pruning filters in convolutional layers has been widely used to reduce the number of parameters required in the convolutional neural networks (CNN) model. However, it fails to fully prune redundant filters for different layers whose redundant filters vary with layers. To overcome this disadvantage, we propose a new backward filter pruning algorithm using a sorted bipartite graph- and binary search-based (SBGBS-based) clustering and decreasing pruning rate (DPR) approach. We first represent each filter of the last layer by a bipartite graph 𝐾1–𝑛, with one root mean set and one 𝑛-weight set, where 𝑛 denotes the number of weights in the filter. Next, according to the accuracy loss tolerance, an SBGBS-based clustering method is used to partition all filters into clusters as maximal as possible. Then, for each cluster, we retain the filter corresponding to the bipartite graph with the median root mean among 𝑛 root means in the cluster, but we discard the other filters in the same cluster. Following the DPR approach, we repeat the above SBGBS-based filtering pruning approach to the backward layer until all layers are processed. Based on the CIFAR-10 and MNIST datasets, the proposed filter pruning algorithm has been deployed into VGG-16, AlexNet, LeNet, and ResNet. With similar accuracy, the thorough experimental results have demonstrated the substantial parameters and floating-point operations reduction merits of our filter pruning algorithm relative to the existing filter pruning methods.