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 non-differentiable top-k #2

Open
xiewende opened this issue Sep 22, 2022 · 3 comments
Open

about non-differentiable top-k #2

xiewende opened this issue Sep 22, 2022 · 3 comments

Comments

@xiewende
Copy link

xiewende commented Sep 22, 2022

Nice Works!

Being very interested in your work,But I found that your top-k ranking removes the differentiable top-k method and uses softmax instead, what is the reason for this ?

@fnzhan
Copy link
Owner

fnzhan commented Sep 22, 2022

Hi, I have a deadline recently and will include the code of this part early next month.

@xiewende
Copy link
Author

Looking forward to your work!

@fnzhan
Copy link
Owner

fnzhan commented Oct 22, 2022

Hi, I update the implementation of top-k including ot_topk and differentiable_topk in models/networks/ranking_attention.py.
You can also directly test the top-k function in util/topk_test.py.

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

2 participants