|
--- |
|
license: other |
|
license_name: carso-adapted-rail-m |
|
license_link: LICENSE |
|
datasets: |
|
- cifar10 |
|
- cifar100 |
|
- jeremyf/tiny-imagent-200 |
|
metrics: |
|
- accuracy |
|
pipeline_tag: image-classification |
|
--- |
|
|
|
Pre-trained models for the paper *"Carefully Blending Adversarial Training and Purification Improves Adversarial Robustness"* (Ballarin et al., 2024) |
|
|
|
**Developed by:** [Emanuele Ballarin](https://ballarin.cc/), [Alessio Ansuini](https://areasciencepark-rit.gitlab.io/lade/alessio.ansuini/), [Luca Bortolussi](https://ai-lab.units.it/?page_id=139) |
|
**Repository:** [`github.com/emaballarin/CARSO`](https://github.com/emaballarin/CARSO) |
|
**Paper:** [*"Carefully Blending Adversarial Training and Purification Improves Adversarial Robustness"* (Ballarin et al., 2024)](https://arxiv.org/abs/2306.06081) |
|
|
|
**Citation (BibTeX):** |
|
```bibtex |
|
@misc{ballarin2023carefully, |
|
title={Carefully Blending Adversarial Training and Purification Improves Adversarial Robustness}, |
|
author={Emanuele Ballarin and Alessio Ansuini and Luca Bortolussi}, |
|
year={2023}, |
|
eprint={2306.06081}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CV} |
|
} |
|
``` |
|
|
