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View Code? Open in Web Editor NEW[ICML 2022] RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression
License: Other
[ICML 2022] RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression
License: Other
thank you for your greate job!
if i would like to apply this to d-dim(d>1) regression task(target has d-min), how should change the ranksim.py ?
Hi, interesting work.
Where is the code about set M in batch to make each label occurs at most once?
Why is the similarity function (sigma^y) used to calculate the similarity matrix (S^y) in the label-space negative absolute distance instead of absolute distance?
As far as I know, the similarity function used in feature space is cosine similarity, which has a range of -1 to 1, where -1 indicates that the two quantities are opposite, 0 indicates that they are orthogonal, and 1 indicates that they are in the same direction.
What problems would it cause if the absolute distance was directly used to calculate the similarity function?
Or, what are the considerations for using the negative absolute distance in the paper?
Thanks :))
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