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MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning

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arxiv 1903.10258 v3 pith:BEWLC6LO submitted 2019-03-25 cs.CV

Zechun Liu , Haoyuan Mu , Xiangyu Zhang , Zichao Guo , Xin Yang , Tim Kwang-Ting Cheng , Jian Sun This is my paper · ORCID
classification cs.CV
keywords networkpruningnetsearchmetanetworksprunedpruningautomatic
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In this paper, we propose a novel meta learning approach for automatic channel pruning of very deep neural networks. We first train a PruningNet, a kind of meta network, which is able to generate weight parameters for any pruned structure given the target network. We use a simple stochastic structure sampling method for training the PruningNet. Then, we apply an evolutionary procedure to search for good-performing pruned networks. The search is highly efficient because the weights are directly generated by the trained PruningNet and we do not need any finetuning at search time. With a single PruningNet trained for the target network, we can search for various Pruned Networks under different constraints with little human participation. Compared to the state-of-the-art pruning methods, we have demonstrated superior performances on MobileNet V1/V2 and ResNet. Codes are available on https://github.com/liuzechun/MetaPruning.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Resource-Aware Neural Network Pruning Using Graph-based Reinforcement Learning

    cs.LG 2025-09 conditional novelty 5.0 of 10

    A graph-attention RL agent with a binary channel-level action space and a self-competition reward prunes CNNs at fixed FLOPs budgets, giving competitive but not uniformly state-of-the-art accuracy.

  2. Efficient Deep Neural Networks

    cs.CV 2019-08 conditional novelty 4.0 of 10

    A dissertation showing that deep learning can be made practical on edge devices through four complementary routes: model, data, hardware, and design efficiency.

  3. On Accelerating Edge AI: Optimizing Resource-Constrained Environments

    cs.LG 2025-01 conditional novelty 2.0 of 10

    The paper argues that model compression, neural architecture search, and compiler optimizations work together to accelerate edge AI, but it provides no new experimental evidence.

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