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Bayesian Optimization-Based Global Optimal Rank Selection for Compression of Convolutional Neural Networks, IEEE Access

License: MIT License

Python 100.00%
cnn-compression rank-selection bayesian-optimization cnn pytorch tensorly neural-network-compression tucker low-rank network-acceleration model-acceleration model-compression network-compression-acceleration bayesopt gpyopt convolutional-neural-networks

cnn_compression_rank_selection_bayesopt's Introduction

BayesOpt-based Global Optimal Rank Selection for Compression of CNNs (Pytorch Implementation)

T. Kim, J. Lee and Y. Choe, "Bayesian Optimization-Based Global Optimal Rank Selection for Compression of Convolutional Neural Networks," in IEEE Access, vol. 8, pp. 17605-17618, 2020, doi: 10.1109/ACCESS.2020.2968357.

Abstract

Finding the optimal rank is a crucial problem beacause the rank is the only hyperparameter for controlling computational complexity and accuracy in compressed CNNs. To solve this problem, we propose a global optimal rank selection method based on Bayesian optimization. By utilizing both a simple objective function and a proper optimization scheme, the proposed method produces a global optimal rank that provides a good trade-off between computational complexity and accuracy degradation.

Usage

  • Decompose a pretrained vgg16 model: python main.py
  • torch: 1.0.0 version
  • tensorly: 0.4.5 version
  • GPyOpt: 1.2.5 version

Reference

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cnn_compression_rank_selection_bayesopt's Issues

CP rank selection

Why there is no experiments and implementation of CP decomposition rank selection? Paper itself mentions it, but Tucker-2 treats as more preferrable. Or do you have got any issues concerning CP optimization for CNN layers?

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