  Channel pruning is widely accepted to accelerate modern convolutional neural
networks (CNNs). The resulting pruned model benefits from its immediate
deployment on general-purpose software and hardware resources. However, its
large pruning granularity, specifically at the unit of a convolution filter,
often leads to undesirable accuracy drops due to the inflexibility of deciding
how and where to introduce sparsity to the CNNs. In this paper, we propose
REPrune, a novel channel pruning technique that emulates kernel pruning, fully
exploiting the finer but structured granularity. REPrune identifies similar
kernels within each channel using agglomerative clustering. Then, it selects
filters that maximize the incorporation of kernel representatives while
optimizing the maximum cluster coverage problem. By integrating with a
simultaneous training-pruning paradigm, REPrune promotes efficient, progressive
pruning throughout training CNNs, avoiding the conventional
train-prune-finetune sequence. Experimental results highlight that REPrune
performs better in computer vision tasks than existing methods, effectively
achieving a balance between acceleration ratio and performance retention.
