FGGP: Fixed-Rate Gradient-First Gradual Pruning

TL;DR

FGGP introduces a novel fixed-rate gradient-first gradual pruning method that effectively sparsifies neural networks while maintaining or surpassing their accuracy, validated on CIFAR-10 with multiple architectures and sparsity levels.

Contribution

The paper proposes a fixed-rate gradient-first magnitude-next pruning strategy that outperforms existing methods in neural network sparsification tasks.

Findings

FGGP outperforms state-of-the-art pruning methods in most settings.

FGGP can surpass the accuracy of dense networks at high sparsity levels.

Validated on CIFAR-10 with VGG-19 and ResNet-50 architectures.

Abstract

In recent years, the increasing size of deep learning models and their growing demand for computational resources have drawn significant attention to the practice of pruning neural networks, while aiming to preserve their accuracy. In unstructured gradual pruning, which sparsifies a network by gradually removing individual network parameters until a targeted network sparsity is reached, recent works show that both gradient and weight magnitudes should be considered. In this work, we show that such mechanism, e.g., the order of prioritization and selection criteria, is essential. We introduce a gradient-first magnitude-next strategy for choosing the parameters to prune, and show that a fixed-rate subselection criterion between these steps works better, in contrast to the annealing approach in the literature. We validate this on CIFAR-10 dataset, with multiple randomized initializations on both VGG-19 and ResNet-50 network backbones, for pruning targets of 90, 95, and 98% sparsity and for both initially dense and 50% sparse networks. Our proposed fixed-rate gradient-first gradual pruning (FGGP) approach outperforms its state-of-the-art alternatives in most of the above experimental settings, even occasionally surpassing the upperbound of corresponding dense network results, and having the highest ranking across the considered experimental settings.