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

• URL: http://arxiv.org/abs/2002.04809v1
• Date: Wed, 12 Feb 2020 05:38:42 GMT
• Title: Lookahead: A Far-Sighted Alternative of Magnitude-based Pruning
• Authors: Sejun Park, Jaeho Lee, Sangwoo Mo, Jinwoo Shin
• Abstract summary: Magnitude-based pruning is one of the simplest methods for pruning neural networks. 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.
• Score: 83.99191569112682
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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