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Hi.
This is Jungmin Kwon, one of the authors of Adaptive Sharpness Aware Minimization (ASAM).
We really appreciate your great implementation!
I have performed cifar10 tests with your code and we found that ASAM with rho=2.0 shows the best accuracy among [0.5, 1.0, 2.0, 5.0].
The test error rates obtained from the grid search are as follows:
rho
Test error rate
0.5
2.75 %
1.0
2.69 %
2.0
2.55 %
5.0
2.90 %
In our implementation without bias (or beta for BatchNorm) normalization (https://github.com/SamsungLabs/ASAM), ASAM with rho=0.5 shows the best accuracy (2.37 % for WRN16-8), so we performed all the cifar10 tests with rho=0.5.
If you don't mind, could you update the table of test error rate with the result of rho=2.0 (2.55 %)?
Thank you.
The text was updated successfully, but these errors were encountered:
thank you very much for working on ASAM, it's a very clever improvement of the original SAM. I've updated the results, thanks for running the grid search! :)
Hi.
This is Jungmin Kwon, one of the authors of Adaptive Sharpness Aware Minimization (ASAM).
We really appreciate your great implementation!
I have performed cifar10 tests with your code and we found that ASAM with rho=2.0 shows the best accuracy among [0.5, 1.0, 2.0, 5.0].
The test error rates obtained from the grid search are as follows:
In our implementation without bias (or beta for BatchNorm) normalization (https://github.com/SamsungLabs/ASAM), ASAM with rho=0.5 shows the best accuracy (2.37 % for WRN16-8), so we performed all the cifar10 tests with rho=0.5.
If you don't mind, could you update the table of test error rate with the result of rho=2.0 (2.55 %)?
Thank you.
The text was updated successfully, but these errors were encountered: