"Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software Deployment" has been accepted by ASE2022 !!! [Arxiv] [ASE]

SafeCompress Framework

MIA-SafeCompress

This code is an instance of our SafeCompress framework called MIA-SafeCompress against membership inference attacks (MIAs)

Requirements

• Python 3.7
• pytorch 1.8
• cuda 10.2
• datetime
• numpy
• torchvision
• itertools

Quick Start

We take vgg with sparsity=0.05 on CIFAR100 for example You can obtain the task acuracy (Task Acc) for vgg by running the following command:

python main.py --sparse --seed 18 --sparse_init ERK  --multiplier 1 --lr 0.1 --density 0.05 --update_frequency 4000 --epochs 300 --model vgg-c --data cifar100 --decay_frequency 30000 --batch-size 128 --n_class 100

To perform membership inference attacks (to obtain MIA Acc), you can run:

python mia_main.py --density 0.05 --epochs 100 --model vgg-c --data cifar100 --batch-size 128 --n_class 100

if it is helpful, please cite our paper:

@inproceedings{zhu2022safety,
  title={Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software Deployment},
  author={Zhu, Jie and Wang, Leye and Han, Xiao},
  booktitle={Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering},
  pages={1--13},
  year={2022}
}

Acknowledgement

This code is based on ITOP