stanford-futuredata / selection-via-proxy Goto Github PK

Hi,
I am getting exactly the same results for k-centers & random coresets with preact20 on cifar10.

I used preact20 as a target and proxy.

Maybe you have changed something in the code after all the experiments.

Dear authors,

I have a question regarding the implementation of the greedy k-center algorithm.

When I run the greedy k-center algorithm with your code, I always get 1000 (which corresponds to the size of the initial subset) more points than the number I specified through the "subset" argument.

Could you explain why this might be happening?

Thank you.

Thanks for sharing this very interesting work. Right now, no license has been included in the repo, meaning all rights are reserved (https://opensource.stackexchange.com/questions/1720/what-can-i-assume-if-a-publicly-published-project-has-no-license), and it cannot be used by others.

Could you perhaps consider including some form of open-source license?

Hi, thank you for a really interesting repo.

I was curious would it be the same for K-centers if we use embedding instead of activations?

Also, did you test only preact resnets or resnet18 would be as good as preact resnet for K-centers?

Thanks!

When using forgetting_events as selection method, if the training of proxy model is interrupted, do you need to record the status of forgetting_events?

Kindly, provide the code and details of the spearman's correlation calculation.