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

Optimisation of MI loss implementation #145

Open
qiuhuaqi opened this issue Mar 1, 2024 · 1 comment
Open

Optimisation of MI loss implementation #145

qiuhuaqi opened this issue Mar 1, 2024 · 1 comment
Labels
enhancement New feature or request p1-important Important to handle

Comments

@qiuhuaqi
Copy link
Collaborator

qiuhuaqi commented Mar 1, 2024

It has been noted by some collaborators that the MI implementation (here) in this library is slightly slower and less easy to tune than De Vos's implementation in TorchIR (https://github.com/BDdeVos/TorchIR/blob/main/torchir/metrics.py#L74). The TorchIR implementation also exposes the Parzen window width as a parameter for users to choose, while we automatically derive this from the choice of number of bins by setting the Gaussian kernel's FWHM as bin width. We could experiment with this as well.

This issue is to track experiments and optimisation.

@qiuhuaqi qiuhuaqi changed the title Optimisation of MI loss Optimisation of MI loss implementation Mar 1, 2024
@aschuh-hf aschuh-hf added enhancement New feature or request p1-important Important to handle labels Mar 7, 2024
@aschuh-hf
Copy link
Collaborator

Thanks for relaying the feedback and helping to improve this! I've added TorchIO to the list of related projects.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
enhancement New feature or request p1-important Important to handle
Projects
None yet
Development

No branches or pull requests

2 participants