Channel pruning enables model acceleration by removing channels from convolutional neural networks (CNNs). However, many existing methods adopt a “hard removal” strategy that directly removing low-importance channels, leading to severe feature loss and accuracy degradation. To address this issue, we propose Reconstruction and Consolidation Pruning (RCP), a pruning framework that decouples the pruning process into a pruning-training phase and an inference phase. During pruning training, RCP generates a pruning strategy based on channel importance under a global pruning rate constraint, and constructs a feature reloading mechanism. This mechanism utilizes a learnable 1×1 compensation convolution to adaptively transfer and fuse discriminative features hidden in the pruned channels into the retained channels. In the inference phase, RCP adopts a linear reparameterization strategy to seamlessly consolidate the compensation branches into the main network branch without loss of performance, ensuring zero additional operator overhead during inference. This reversible structural transformation ensures that the training-time augmented architecture and the inference-time compact architecture are functionally identical under linear consolidation. Experimental results show that at 50% FLOPs reduction, RCP incurs only a 0.84% accuracy drop on ResNet-50 (ImageNet-1K), while at 53% FLOPs reduction it achieves a 0.07% accuracy improvement for ResNet-56 (CIFAR-10), validating the proposed method’s effectiveness and superiority under high compression rates.
Zhang et al. (Mon,) studied this question.