MLPruner: pruning convolutional neural networks with automatic mask learning.

ABSTRACT: In recent years, filter pruning has been recognized as an indispensable technique for mitigating the significant computational complexity and parameter burden associated with deep convolutional neural networks (CNNs). To date, existing methods are based on heuristically designed pruning metrics or implementing weight regulations to penalize filter parameters during the training process. Nevertheless, human-crafted pruning criteria tend not to identify the most critical filters, and the introduction of weight constraints can inadvertently interfere with weight training. To rectify these obstacles, this article introduces a novel mask learning method for autonomous filter pruning, negating requirements for weight penalties. Specifically, we attribute a learnable mask to each filter. During f

SUBMITTER: Chen S 

PROVIDER: S-EPMC12453823 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature