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Added new models.

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Sven Gowal
2022-03-15 16:59:51 +00:00
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This repository contains the code needed to evaluate models trained in This repository contains the code needed to evaluate models trained in
[Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples](https://arxiv.org/abs/2010.03593) [Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples](https://arxiv.org/abs/2010.03593)
(Gowal et al., 2020) and in (Gowal et al., 2020), in
[Fixing Data Augmentation to Improve Adversarial Robustness](https://arxiv.org/abs/2103.01946) [Fixing Data Augmentation to Improve Adversarial Robustness](https://arxiv.org/abs/2103.01946)
(Rebuffi et al., 2021). (Rebuffi et al., 2021) and in
[Improving Robustness using Generated Data](https://arxiv.org/abs/2110.09468)
(Gowal et al., 2021).
## Contents ## Contents
@@ -50,6 +52,14 @@ The following table contains the models from **Rebuffi et al., 2021**.
| CIFAR-100 | &#8467;<sub>&infin;</sub> | 8 / 255 | WRN-28-10 | &#x2717; | 62.41% | 32.06% | [jax](https://storage.googleapis.com/dm-adversarial-robustness/cifar100_linf_wrn28-10_cutmix_ddpm.npy), [pt](https://storage.googleapis.com/dm-adversarial-robustness/cifar100_linf_wrn28-10_cutmix_ddpm.pt) | CIFAR-100 | &#8467;<sub>&infin;</sub> | 8 / 255 | WRN-28-10 | &#x2717; | 62.41% | 32.06% | [jax](https://storage.googleapis.com/dm-adversarial-robustness/cifar100_linf_wrn28-10_cutmix_ddpm.npy), [pt](https://storage.googleapis.com/dm-adversarial-robustness/cifar100_linf_wrn28-10_cutmix_ddpm.pt)
| CIFAR-100 | &#8467;<sub>&infin;</sub> | 8 / 255 | ResNet-18 | &#x2717; | 56.87% | 28.50% | [jax](https://storage.googleapis.com/dm-adversarial-robustness/cifar100_linf_resnet18_ddpm.npy), [pt](https://storage.googleapis.com/dm-adversarial-robustness/cifar100_linf_resnet18_ddpm.pt) | CIFAR-100 | &#8467;<sub>&infin;</sub> | 8 / 255 | ResNet-18 | &#x2717; | 56.87% | 28.50% | [jax](https://storage.googleapis.com/dm-adversarial-robustness/cifar100_linf_resnet18_ddpm.npy), [pt](https://storage.googleapis.com/dm-adversarial-robustness/cifar100_linf_resnet18_ddpm.pt)
The following table contains additional models from **Gowal et al., 2021**.
| dataset | norm | radius | architecture | extra data | clean | robust | link |
|---|:---:|:---:|:---:|:---:|---:|---:|:---:|
| CIFAR-10 | &#8467;<sub>&infin;</sub> | 8 / 255 | WRN-70-16 | &#x2717; | 88.74% | 66.11% | [jax](https://storage.googleapis.com/dm-adversarial-robustness/cifar10_linf_wrn70-16_cutmix_ddpm_100m.npy), [pt](https://storage.googleapis.com/dm-adversarial-robustness/cifar10_linf_wrn70-16_cutmix_ddpm_100m.pt)
| CIFAR-10 | &#8467;<sub>&infin;</sub> | 8 / 255 | WRN-70-16 | &#x2717; | 87.50% | 63.44% | [jax](https://storage.googleapis.com/dm-adversarial-robustness/cifar10_linf_wrn28-10_ddpm_100m.npy), [pt](https://storage.googleapis.com/dm-adversarial-robustness/cifar10_linf_wrn28-10_ddpm_100m.pt)
| CIFAR-10 | &#8467;<sub>&infin;</sub> | 8 / 255 | ResNet-18 | &#x2717; | 87.35% | 58.63% | [jax](https://storage.googleapis.com/dm-adversarial-robustness/cifar10_linf_resnet18_ddpm_100m.npy), [pt](https://storage.googleapis.com/dm-adversarial-robustness/cifar10_linf_resnet18_ddpm_100m.pt)
### Installing ### Installing
The following has been tested using Python 3.9.2. The following has been tested using Python 3.9.2.
@@ -125,8 +135,9 @@ out to Sven Gowal directly.
### Generated datasets ### Generated datasets
Rebuffi et al. (2021) use samples generated by a Denoising Diffusion Rebuffi et al. (2021) and Gowal et al. (2021) use samples generated by a
Probabilistic Model [(DDPM; Ho et al., 2020)](https://arxiv.org/abs/2006.11239) Denoising Diffusion Probabilistic Model
[(DDPM; Ho et al., 2020)](https://arxiv.org/abs/2006.11239)
to improve robustness. The DDPM is solely trained on the original training data to improve robustness. The DDPM is solely trained on the original training data
and does not use additional external data. The following table links to datasets and does not use additional external data. The following table links to datasets
of 1M **generated** samples for CIFAR-10, CIFAR-100 and SVHN. of 1M **generated** samples for CIFAR-10, CIFAR-100 and SVHN.
@@ -172,6 +183,18 @@ and/or
} }
``` ```
and/or
```
@article{gowal2021generated,
title={Improving Robustness using Generated Data},
author={Gowal, Sven and Rebuffi, Sylvestre-Alvise and Wiles, Olivia and Stimberg, Florian and Calian, Dan A. and Mann, Timothy},
journal={arXiv preprint arXiv:2110.09468},
year={2021},
url={https://arxiv.org/pdf/2110.09468}
}
```
## Disclaimer ## Disclaimer
This is not an official Google product. This is not an official Google product.