Abstract
This is the official code for the ICML 2024 paper "Adversarial Robustness Limits via Scaling-Law and
Human-Alignment Studies". This code extends that of Wang et al. (2023) to facilitate state-of-the-art
CIFAR-10 adversarial robustness, via training of WideResNet models on various large synthetic datasets.
The code also facilitates derivation of the various scaling laws put forth in our ICML paper, which we use
to compute efficient training settings.
- Developers:
-
Bartoldson, Brian [1]
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Release Date:
- 2024-05-21
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Version:
- 1.0
- Licenses:
-
MIT License
- Sponsoring Org.:
-
USDOE National Nuclear Security Administration (NNSA)Primary Award/Contract Number:AC52-07NA27344
- Code ID:
- 134198
- Site Accession Number:
- LLNL-CODE-866411
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Country of Origin:
- United States
Citation Formats
Bartoldson, Brian R.
Adversarial Robustness Limits.
Computer Software.
https://github.com/bbartoldson/Adversarial-Robustness-Limits.
USDOE National Nuclear Security Administration (NNSA).
21 May. 2024.
Web.
doi:10.11578/dc.20240710.2.
Bartoldson, Brian R.
(2024, May 21).
Adversarial Robustness Limits.
[Computer software].
https://github.com/bbartoldson/Adversarial-Robustness-Limits.
https://doi.org/10.11578/dc.20240710.2.
Bartoldson, Brian R.
"Adversarial Robustness Limits." Computer software.
May 21, 2024.
https://github.com/bbartoldson/Adversarial-Robustness-Limits.
https://doi.org/10.11578/dc.20240710.2.
@misc{
doecode_134198,
title = {Adversarial Robustness Limits},
author = {Bartoldson, Brian R.},
abstractNote = {This is the official code for the ICML 2024 paper "Adversarial Robustness Limits via Scaling-Law and
Human-Alignment Studies". This code extends that of Wang et al. (2023) to facilitate state-of-the-art
CIFAR-10 adversarial robustness, via training of WideResNet models on various large synthetic datasets.
The code also facilitates derivation of the various scaling laws put forth in our ICML paper, which we use
to compute efficient training settings.},
doi = {10.11578/dc.20240710.2},
url = {https://doi.org/10.11578/dc.20240710.2},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20240710.2}},
year = {2024},
month = {may}
}