This is the repository for the paper Adversarially Robust Generalization Just Requires More Unlabeled Data submitted to NeurIPS 2019 (paper link).
File | Information |
---|---|
adv_train.py | Original adversarial training |
transductive.py | Transductive setting |
5k10k.py | 5k/10k experiment |
model.py | WResNet-32 model |
utils.py | Helper functions for train/test |
Checkpoints can be downloaded here. Use the following code to load the checkpoints:
checkpoint = torch.load('checkpoint.t7')
net.load_state_dict(checkpoint['net'])
The following checkpoints are included.
File | Information |
---|---|
5k-{0.0,0.1,0.2,0.3}.t7 | 5k experiment with lambda = 0.0,0.1,0.2,0.3 |
10k-{0.0,0.1,0.2,0.3}.t7 | 10k experiment with lambda = 0.0,0.1,0.2,0.3 |
transductive.t7 | Transductive setting with lambda = 0.125 |
pgd7_adv_train.t7 | Original adversarial training |
Please cite our paper with the following BibTeX entry:
@article{DBLP:journals/corr/abs-1906-00555,
author = {Runtian Zhai and
Tianle Cai and
Di He and
Chen Dan and
Kun He and
John E. Hopcroft and
Liwei Wang},
title = {Adversarially Robust Generalization Just Requires More Unlabeled Data},
journal = {CoRR},
volume = {abs/1906.00555},
year = {2019},
url = {http://arxiv.org/abs/1906.00555},
archivePrefix = {arXiv},
eprint = {1906.00555},
timestamp = {Thu, 13 Jun 2019 13:36:00 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1906-00555},
bibsource = {dblp computer science bibliography, https://dblp.org}
}