Lookahead: A Far-sighted Alternative of Magnitude-based PruningDownload PDF

Sep 25, 2019 (edited Mar 11, 2020)ICLR 2020 Conference Blind SubmissionReaders: Everyone
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  • 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
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