Mismatch of the training data augmentation between QuickStart.ipynb and main.py about unlearnable-examples HOT 1 CLOSED

Hi,

Thanks for your interest in our work.

To reproduce the results in the paper, please check the *.sh in the scripts folder for each corresponding experiment.

For Q1: I believe both are x'=x+noise. The train_transform applies in __getitem__() function which is called by the DataLoader during iterating. In both dataset.py QuickStart.ipynb, it modifies the self.data in CIFAR10 prior to feeding it to the DataLoader, so they should be the same. That is x'=x+noise, and the applies the augmentations in training loops.

For Q2: I did not consider using augmentations during generating the noise at the start, and it can already generate effective noise. I'm not sure if this improves/degrade the effectiveness. My guess is it would improve, worth a shot.

Best,

from unlearnable-examples.