Comments (3)
Hi - thanks for your interets. The choice of kernel
, ks
, sigma
should be related to the task of interest. For example, in age estimation, with the minimum bin size of 1 (the minimum age resolution you care is 1 year), you would not expect a very large kernel size considering similar nearby ages. In Appendix E.3 of our paper, we studied some choices of ks
and sigma
, and it might give you some sense on what values are good for different tasks.
from imbalanced-regression.
Well, I applied LDS and FDS to my age estimation model and found that MAE was hovering at 10 after the introduction of FDS and could not be reduced. This problem did not occur when LDS was used only without FDS. Do you have any idea what might have caused this?
from imbalanced-regression.
I'm not sure --- we did not see such phenomenon in the provided datasets (e.g., IMDB-WIKI-DIR, etc.). Maybe you need to tune the hyper-parameters of FDS a bit (still, kernel
, ks
, sigma
), and see if the problem still exists.
from imbalanced-regression.
Related Issues (20)
- Implementation of SMOGN and RRT for deep regression HOT 1
- How to use this method in a multi-dimension regression problem? HOT 3
- Hi, question about the Appendix HOT 2
- Hi, confusion about the computation of the feature statistics similarity (mean&variance) HOT 2
- FDS performs worse than vanilla HOT 1
- About SHHS-DIR Code HOT 1
- prediction value processing HOT 4
- how to update statistics in FDS HOT 1
- I have a question about training a model using RRT HOT 1
- Applying LDS/FDS to classic machine learning models HOT 2
- Question About Paper
- On the implementation of SMOTER and SMOGN algorithms in the paper HOT 1
- The reproduced benchmark and model seem to be damaged HOT 2
- about test error HOT 2
- The problem of regression to the pixel value of the picture HOT 3
- SHHS dataset HOT 1
- bucket_num bucket_start HOT 1
- Negative Age HOT 1
- move get_transform to __init__ HOT 1
- How Interpolation and Extrapolation works? HOT 1
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from imbalanced-regression.