- Tensorflow 2.3
- Keras 2.4.3
├── baselines # code for baselines, CES, PACE, Meta-set, Prediction-based, and Random
├── models # how to use dataset and prepare models
├── results
├── figures_for_rq2 # all figures of RQ2
├── utils # Tiny-ImageNet generator
├── Aries.py # Aries source code
├── example.py # example of using Aries
- PACE code is from https://github.com/pace2019/pace
- CES code is from https://github.com/Lizn-zn/DNNOpAcc
- Meta-set code is from https://github.com/Simon4Yan/Meta-set
- CIFAR10-C data: https://zenodo.org/record/2535967#.YfRacRNKhQI
- Tiny-ImageNet-C data: https://zenodo.org/record/2469796#.YnPl6hMzZhE
If you use the code in your research, please cite:
@article{hu2022efficient,
title={Aries: Efficient Testing of Deep Neural Networks via Labeling-Free Accuracy Estimation},
author={Hu, Qiang and Guo, Yuejun and Xie, Xiaofei and Cordy, Maxime and Ma, Lei and Papadakis, Mike and Traon, Yves Le},
journal={arXiv preprint arXiv:2207.10942},
year={2022}
}