  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.
