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

Combination of supervised and weakly-supervised data #233

Open
bellet opened this issue Jul 5, 2019 · 2 comments
Open

Combination of supervised and weakly-supervised data #233

bellet opened this issue Jul 5, 2019 · 2 comments
Labels
new feature

Comments

@bellet
Copy link
Member

bellet commented Jul 5, 2019 •
edited
Loading

We could easily allow users to fit weakly-supervised algorithms on a combination of label supervision (from which we generate constraints as in supervised versions) and additional weak supervision specified by the user
@hansen7
Copy link
Contributor

hansen7 commented Jul 22, 2019

cool! actually the semi-supervised means to use unlabelled(or without relative comparison etc.) data to construct the loss terms, such as entropy, but how to build up the optimisation process based on this arbitrary loss/constraint?

@RobinVogel RobinVogel mentioned this issue Nov 28, 2019
Closed
@bellet bellet changed the title Semi-supervised versions of weakly-supervised algorithms Combination of supervised and weakly-supervised data Dec 5, 2019
@bellet
Copy link
Member Author

bellet commented Dec 5, 2019

cool! actually the semi-supervised means to use unlabelled(or without relative comparison etc.) data to construct the loss terms, such as entropy, but how to build up the optimisation process based on this arbitrary loss/constraint?

Yep, I have rephrased the issue

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Assignees
No one assigned
Labels
new feature
Projects
None yet
Milestone
No milestone
2 participants
@bellet @hansen7