PyTorch Implementation of the SuRP algorithm by the authors of the AISTATS 2022 paper "An Information-Theoretic Justification for Model Pruning".

An Information-Theoretic Justification for Model Pruning
Berivan Isik, Tsachy Weissman, Albert No
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.

To train the baseline model to be compressed, set trainer=Classifier. To try this for ResNet-20, run:

python3 main.py --trainer=Classifier --config=cifar_resnet20/config.yaml

To test the baseline model, run:

python3 main.py --trainer=Classifier --config=cifar_resnet20/config.yaml --test

To compress the baseline model with SuRP non-iteratively, change the experiment id exp_id of the target model and target sparsity ratio sparsity: [sparsity of the input model, target sparsity] in the recon.yaml file accordingly. Then, run:

python3 main.py --trainer=Reconstruction --config=cifar_resnet20/recon.yaml

To compress the baseline model with SuRP iteratively, apply SuRP several times following a sparsity schedule. Each time, modify exp_id and sparsity: [sparsity of the input model, target sparsity], accordingly. To retrain the sparse models before applying SuRP again, set retrain: True. And run:

python3 main.py --trainer=ReconFromFile --config=cifar_resnet20/recon.yaml

If you find this work useful in your research, please consider citing our paper:

@InProceedings{pmlr-v151-isik22a,
  title = 	 { An Information-Theoretic Justification for Model Pruning },
  author =       {Isik, Berivan and Weissman, Tsachy and No, Albert},
  booktitle = 	 {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics},
  pages = 	 {3821--3846},
  year = 	 {2022},
  volume = 	 {151},
  series = 	 {Proceedings of Machine Learning Research},
  publisher =    {PMLR},
  url = 	 {https://proceedings.mlr.press/v151/isik22a.html}
}