This is supplemental material containing the code and training logs for the paper "Overfitting in adversarially robust deep learning" submitted to ICML 2020.
The experiments for CIFAR10, CIFAR100, and SVHN are in train_cifar.py, train_cifar100.py, train_svhn.py respectively.
- CIFAR10 training with semisupervised data is done in
train_cifar_semisupervised_half.py, and uses the 500K pseudo-labeled TinyImages data from https://github.com/yaircarmon/semisup-adv - TRADES training is done with the repository located at https://github.com/yaodongyu/TRADES, with the only modification being the changes to the learning rate schedule to train to convergence (to decay at epochs 100 and 150 out of 200 total epochs).
For ImageNet training, we used the repository located at https://github.com/MadryLab/robustness with no modifications. The resulting logged data is stored in .pth which can be loaded with torch.load and are simply dictionaries of logged data.
Logs are all located in the experiments folder, and each subfolder corresponds to a set of experiments carried in the paper.
Model weights for the following models can be found at https://drive.google.com/open?id=1xslETwg3nl_Mwaq_kbZiZVo9BnhCcVMG
- The best checkpoints for CIFAR10 WideResNets defined in
wideresnet.py(in for width factor 10 and 20 (from the double descent curve trained against L-infinity) - The best checkpoints for SVHN / CIFAR10 (L2) / CIFAR100 / ImageNet models reported in Table 1 (the ImageNet checkpoints are in the format directly used by https://github.com/MadryLab/robustness). The remaining models are for the Preactivation ResNet18 defined in
preactresnet.py.