kthyeon / fine_official Goto Github PK

Hi, thanks for sharing your implementation.
In your implementation, threr are some code error.
The functions ,get_out_list and get_singular_value_vector, used in FINE_official/dynamic_selection/traintools/gtrobustlosstrain.py is not really so non-existent.
Can you give me details about these functions?

I found some hyperparameters are different from the description in the paper, "All hyper-parameters settings are the same with [25], even for the clean probability threshold.". For example, the threshold in the code is 0.6, but in DivideMix the threshold is 0.5. The number of warmup is also different from DivideMix. Could you provide hyperparameters for reproducing the results?

Hello,

I have read your paper and find it very interesting. However, I may have some confusion about your method. If I understood correctly, the first eigenvector represents the latent distribution of a class, which is similar as the function of a prototype. And I also saw some methods utilize the similarity between a sample and the class-prototype to select clean samples. I would like to know what is the advantage of using the eigenvector over prototypes.

Thanks.

Hi,
thanks for sharing your implementation. I have some questions about it:

1. Does it also work on tabular data?
2. Is the code tailored to the datasets used in the paper or can one apply it to any data?
3. Is it possible to identify the noisy instances (return the noisy IDs or the clean set)?

Thanks!