| --- |
| 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} |
| } |
| ``` |
|
|
