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

About Kernel Estimation losses #27

Open
claroche-gpfw-zz opened this issue Nov 3, 2020 · 0 comments
Open

About Kernel Estimation losses #27

claroche-gpfw-zz opened this issue Nov 3, 2020 · 0 comments

Comments

@claroche-gpfw-zz
Copy link

claroche-gpfw-zz commented Nov 3, 2020

Hi,

Thanks for your paper and code, I really liked the way you built your LR/HR pairs.

In the paper, you use 4 different losses to estimate the kernel based on what was done in KernelGAN. However, you slightly modify the original KernelGAN loss by removing the sparsity loss and by adding the following loss:

Can you explain to me the advantages of such changes in comparison to the classical KernelGAN method?

Thanks,
Charles

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

1 participant