Papers › Lookahead: a Far-Sighted Alternative of Magnitude-based Pruning

Lookahead: a Far-Sighted Alternative of Magnitude-based Pruning

12 Feb 2020ICLR 2020 1arXiv:2002.04809archive 2025-07-28

Sejun Park, Jaeho Lee, Sangwoo Mo, Jinwoo Shin

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.

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