Lookahead: A Far-sighted Alternative of Magnitude-based Pruning

Sejun Park*, Jaeho Lee*, Sangwoo Mo, Jinwoo Shin

TL;DR: We study a multi-layer generalization of the magnitude-based pruning.

Abstract: Magnitude-based pruning is one of the simplest methods for pruning neural networks. Despite its simplicity, magnitude-based pruning and its variants demonstrated remarkable performances for pruning modern architectures. Based on the observation that magnitude-based pruning indeed minimizes the Frobenius distortion of a linear operator corresponding to a single layer, we develop a simple pruning method, coined lookahead pruning, by extending the single layer optimization to a multi-layer optimization. Our experimental results demonstrate that the proposed method consistently outperforms magnitude-based pruning on various networks, including VGG and ResNet, particularly in the high-sparsity regime. See https://github.com/alinlab/lookahead_pruning for codes.

Keywords: network magnitude-based pruning

Code: https://github.com/alinlab/lookahead_pruning

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2002.04809/code)