One Less Reason for Filter Pruning: Gaining Free Adversarial Robustness with Structured Grouped Kernel Pruning (SR-GKP)

This is the official codebase for our NeurIPS 2023 paper (OpenReview). Should you need to cite our paper, please use the following BibTeX:

@inproceedings{zhong2023adv_robust_gkp,
    title={One Less Reason for Filter Pruning: Gaining Free Adversarial Robustness with Structured Grouped Kernel Pruning},
    author={Shaochen Zhong and Zaichuan You and Jiamu Zhang and Sebastian Zhao and Zachary LeClaire and Zirui Liu and Daochen Zha and Vipin Chaudhary and Shuai Xu and Xia Hu},
    booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
    year={2023},
}

Getting started with a runnable Colab Notebook

We provide a quick start notebook to demo how to prune a CIFAR-10-trained ResNet-20 according to SR-GKP specifications, then fine-tune the one-shot pruned model in a vanilla fashion.

To-Do

• [x] Open source SR-GKP's implementation.
• [ ] Add paper highlight & results.
• [ ] Share reported model checkpoints (SR-GKP and others).
• [ ] Clean up and open source replication code for other iterative pruning methods evaluated in the paper.
• [ ] Migrate other one-shot pruning methods under our repo (following the same model definitions).