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.

Sign up for GitHub

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

Jump to bottom

Why the KL Divergence Loss needs to be multiplied by number of classes? #7

Open
tim5go opened this issue Aug 20, 2020 · 0 comments
Open

Why the KL Divergence Loss needs to be multiplied by number of classes? #7

tim5go opened this issue Aug 20, 2020 · 0 comments

Comments

@tim5go
Copy link

tim5go commented Aug 20, 2020

Confident_classifier/src/run_joint_confidence.py

Line 156 in 462db01

KL_loss_fake = F.kl_div(KL_fake_output, uniform_dist)*args.num_classes

I'm bit confused about why you needa multiply the KL divergence loss by the number of classes.
I can't find it from the definition of your paper, could you briefly explain it?
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Assignees
No one assigned
Labels
None yet
Projects
None yet
Milestone
No milestone
Development

No branches or pull requests
1 participant
@tim5go