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.

本文提出了一种新的通道修剪技术 REPrune，利用层次聚类方法在每个通道中识别相似的卷积核，并通过最大化聚类覆盖问题来选择卷积滤波器，结合同时训练和修剪的范式，实现训练过程中的高效修剪，实验结果显示 REPrune 在计算机视觉任务中表现优于现有方法，有效地在加速率和性能保留之间取得平衡。