Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Why do we need to do clamp(delta, lower_limit - X, upper_limit - X)? #11

Closed
hyserendipity opened this issue Aug 4, 2020 · 2 comments

Comments

@hyserendipity
Copy link

No description provided.

@AliLotfi92
Copy link

To make sure the input (X+\delta) to the model is in the legitimate interval.

@hkunzhe
Copy link

hkunzhe commented Feb 7, 2021

They add transforms.Normalize(mean, std) in train_transform instead of adding normalization layer to the model with nn.Sequential. You can find more details in Harry24k/adversarial-attacks-pytorch#1.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

4 participants