Comments (1)
I'm not sure why the results would be different with different pytorch versions, however I'm guessing that the model is catastrophically overfitting in the first case, resulting in zero robust test accuracy. I'd try checking during training whether this is occurring by checking the PGD accuracy on the first training batch of each epoch (i.e. see how we do this in the CIFAR10 training code here). I'd also try reducing the alpha
parameter to avoid catastrophic overfitting if this is indeed what is occurring.
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Related Issues (20)
- torch.where API in MNIST and CIFAR10, ImageNet configuration files HOT 1
- Overwrite of variable i in nested for loop
- Reproduce results HOT 1
- indices HOT 1
- About PGD evaluation HOT 1
- Why do we need to do clamp(delta, lower_limit - X, upper_limit - X)? HOT 2
- Reproduce the results of Free adversarial training.
- invalid key "/xff" when loading model.
- When computing the perturbation, do we need to set model.eval()? HOT 1
- Parameter settings on CIFAR-100 HOT 1
- adversarial attack
- Reproduce the result of CIFAR-10 from the default setting HOT 2
- facing "nan" values during training the model HOT 1
- reproduce problem of imagenet on default set HOT 1
- Can't reproduce MNIST results using current codes HOT 2
- Inconsistent clamping behaviour between CIFAR and MNIST fgsm implementaitions HOT 1
- Parameters of training HOT 1
- Why not using clean samples during training? HOT 1
- Probable gradient accumulation bug in mnist_train.py HOT 1
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