Abstract:

Current pruning methods often face inconsistencies in channel selection criteria,resulting in noticeable disparities in channel selection trends.This discrepancy contributes to the emergence of blind spots within pruning strategies.To address this issue,we propose a channel pruning method via an Adaptive Guidance Mechanism (AGM) optimized by the Dual-Domain Progressive Optimization Algorithm.Specifically,the Guiding Mechanism introduces a regularization term to allocate penalty levels,balancing individual criterion scores with the convergence of multiple criteria.In addition,the Dual-Domain Progressive Optimization Algorithm dynamically adjusts the search strategy based on changes in spatial relationships and the progress of iterative search,flexibly determining the optimal depth and breadth of the guidance mechanism to achieve the best pruning performance.The proposed AGM harmonizes the opposition and unity between the individual perspective-based and overall perspective-based pruning criteria with the optimal depth and breadth of impact,forming a cohesive and comprehensive pruning system.Experimental results demonstrate that the proposed pruning method outperforms existing pruning methods,significantly reducing model parameters and FLOPs with a minimal accuracy loss.For example,it compresses the VGG-16 network on CIFAR-10 to 11.15% of its original size with just a 0.04% accuracy drop.On ImageNet,it reduces ResNet-50 parameters to 27.01% while maintaining a 73.83% accuracy.

Key words: deep learning, convolutional neural network, channel pruning, model compression