Skip to content

Code for ICML2019 Paper "On the Convergence and Robustness of Adversarial Training"

Notifications You must be signed in to change notification settings

YisenWang/dynamic_adv_training

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

7 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Dynamic AdveRsarial Training (Dynamic/DART)

Code for ICML2019 Paper "On the Convergence and Robustness of Adversarial Training"

One Important Message in this paper: To ensure better robustness, it is essential to use adversarial examples with better convergence quality at the later stages of training. Yet at the early stages, high convergence quality adversarial examples are not necessary and may even lead to poor robustness.

Convergence quality is measured by First-Order Stationary Condition (FOSC)

Requirements

  • Python 3.5.2,
  • Tensorflow 1.10.1
  • Keras 2.2.2

Usage

Simply run the code by: python3 train_models.py

Citing this work

If you use this code in your work, please cite the accompanying paper:

@inproceedings{wang2019dynamic,
  title={On the Convergence and Robustness of Adversarial Training},
  author={Wang, Yisen and Ma, Xingjun and Bailey, James and Yi, Jinfeng and Zhou, Bowen and Gu, Quanquan},
  booktitle={International Conference on Machine Learning},
  year={2019}
}

About

Code for ICML2019 Paper "On the Convergence and Robustness of Adversarial Training"

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages