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Add table with CIFAR-100 corruptions, update version on README
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dedeswim committed May 4, 2021
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Expand Up @@ -78,7 +78,7 @@ for a quick introduction. It is also summarized below 👇.

First, install **`RobustBench`**:
```bash
pip install git+https://github.com/RobustBench/robustbench.git@v0.1
pip install git+https://github.com/RobustBench/robustbench.git@v0.2
```

Now let's try to load CIFAR-10 and the most robust CIFAR-10 model from [Carmon2019Unlabeled](https://arxiv.org/abs/1905.13736)
Expand Down Expand Up @@ -253,6 +253,13 @@ You can find all available model IDs in the table below (note that the full lead
| <sub>**9**</sub> | <sub><sup>**Sitawarin2020Improving**</sup></sub> | <sub>*[Improving Adversarial Robustness Through Progressive Hardening](https://arxiv.org/abs/2003.09347)*</sub> | <sub>62.82%</sub> | <sub>24.57%</sub> | <sub>WideResNet-34-10</sub> | <sub>ICML 2020</sub> |
| <sub>**10**</sub> | <sub><sup>**Rice2020Overfitting**</sup></sub> | <sub>*[Overfitting in adversarially robust deep learning](https://arxiv.org/abs/2002.11569)*</sub> | <sub>53.83%</sub> | <sub>18.95%</sub> | <sub>Pre-activation ResNet 18</sub> | <sub>ICML 2020</sub> |

#### Corruptions

| <sub>#</sub> | <sub>Model ID</sub> | <sub>Paper</sub> | <sub>Clean accuracy</sub> | <sub>Robust accuracy</sub> | <sub>Architecture</sub> | <sub>Venue</sub> |
|:---:|---|---|:---:|:---:|:---:|:---:|
| <sub>**1**</sub> | <sub><sup>**Hendrycks2020AugMix_ResNeXt**</sup></sub> | <sub>*[AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty](https://arxiv.org/abs/1912.02781)*</sub> | <sub>78.90%</sub> | <sub>65.54%</sub> | <sub>ResNeXt29_32x4d</sub> | <sub>ICLR 2020</sub> |
| <sub>**2**</sub> | <sub><sup>**Hendrycks2020AugMix_WRN**</sup></sub> | <sub>*[AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty](https://arxiv.org/abs/1912.02781)*</sub> | <sub>76.28%</sub> | <sub>64.63%</sub> | <sub>WideResNet-40-2</sub> | <sub>ICLR 2020</sub> |

## Notebooks
We host all the notebooks at Google Colab:
- [RobustBench: quick start](https://colab.research.google.com/drive/1MQY_7O9vj7ixD5ilVRbdQwlNPFvxifHV): a quick tutorial
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