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RePaIR: Repaired pruning at initialization resilience.

Haocheng Zhao1, Runwei Guan1, Ka Lok Man2

  • 1Institute of Deep Perception Technology, JITRI, 214000, Wuxi, China; Department of Electrical Engineering and Electronics, University of Liverpool, L69 3BX, Liverpool, United Kingdom; Department of School of Advanced Technology, Xi'an Jiaotong-Liverpool University, 215123, Suzhou, China; XJTLU-JITRI Academy of Technology, Xi'an Jiaotong-Liverpool University, 215123, Suzhou, China.

Neural Networks : the Official Journal of the International Neural Network Society
|January 1, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces Repaired Initialization (ReI) and Repaired Pruning at Initialization Resilience (RePaIR) to enhance neural network training. These methods improve model robustness and accuracy, especially for pruned models, by considering weight suitability during initialization.

Keywords:
LipschitzNeural networkPruning at initializationUnstructured pruning

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Area of Science:

  • Deep Learning
  • Neural Network Pruning
  • Model Optimization

Background:

  • Neural network models have grown in size, increasing interest in pruning techniques.
  • Unstructured pruning offers fine-grained sparsity for inference acceleration but can risk underfitting.
  • Existing pruning methods often neglect the suitability of retained weights for training.

Purpose of the Study:

  • To analyze the impact of Lipschitz initialization on neural network training.
  • To propose novel algorithms, Repaired Initialization (ReI) and Repaired Pruning at Initialization Resilience (RePaIR), to mitigate underfitting and improve training robustness.
  • To enhance existing pruning algorithms like SynFlow for deeper models.

Main Methods:

  • Analyzing the effect of Lipschitz constants on model initialization and training.
  • Developing the Repaired Initialization (ReI) algorithm for modules with BatchNorm.
  • Proposing the Repaired Pruning at Initialization Resilience (RePaIR) algorithm for unstructured pruned models.
  • Introducing Repair SynFlow (ReSynFlow) by incorporating Lipschitz scaling into SynFlow.

Main Results:

  • ReI and RePaIR improved training robustness for both unpruned and pruned models.
  • Achieved up to 1.7% accuracy gain with the same sparse pruning mask on TinyImageNet using RePaIR.
  • ReSynFlow improved the maximum compression rate and accuracy (up to 1.3%) for deeper models compared to SynFlow on TinyImageNet.

Conclusions:

  • Lipschitz initialization strategies can significantly enhance neural network training and pruning.
  • ReI and RePaIR offer effective solutions for improving model resilience and performance.
  • ReSynFlow provides a viable method for compressing deeper neural networks more effectively.