# Revisiting Residual Networks for Adversarial Robustness

Implementation for ICLR2023 Submission ID-654 "Revisiting Residual Networks for Adversarial Robustness"


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## Compound Scaling
```
python compound_scaling.py --expected_depth 20 --depth_ratio 0.7 --block_type basic_block --save_name arch_001
```

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## Reproduce results from the paper
We provide several examples for training at scripts/train_robust_cifar10_robustresnet.sh. 

## Evaluations of the robustness of trained model
We provide several examples for evaluating robustness under several attackers at  scripts/eval_robust_cifar10_robustresnet.sh.

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## Part of the code is based on the following repo:
  - MART: https://github.com/YisenWang/MART
  - TREADES: https://github.com/yaodongyu/TRADES
  - RST: https://github.com/yaircarmon/semisup-adv
  - AutoAttack: https://github.com/fra31/auto-attack
  - RobustWRN: https://github.com/HanxunH/RobustWRN
