Comments (2)
Hi Mr.kaidic
Firstly, Thanks for you sharing your code and paper.
I read your paper and used your code to be impressed.
As i read this paper, there are comments for 2 cases.
- training set imbalance, test set is not, and their distributions are different.
- training set imbalance, test set is also imbalnced, and their distributions are different.
But you know, unfortunately real data is more imabalanced and challenging.
- My data distribution is not only imbalanced in training set but in test set too.
I mean both sets are imbalanced and have same distribution.- and in my dataset some of classes even got 1 instance for 1 class. ( extremely low )
Then Here's my question to you.
Do you think this LDAM-DRW loss also works to this dataset?
I'm doing experiments changing betas, and delta j (m_list) so on.i would be very much appreciated if you answer to me !
Ty so much kaidic :)
I am also considering the imbalance of test set, does LDAM-DRW work effectively when it used for imbalanced test set.
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My data distribution is similar with yours ! Have you found some hyperparameter settings work? Thanks a lot! :)
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Related Issues (17)
- Whats ETA on code release? HOT 1
- AttributeError: 'IMBALANCECIFAR10' object has no attribute 'data' HOT 1
- more details about your paper
- About the LDAM Loss HOT 4
- Can not achieve similar results For Tiny ImageNet
- the learning rate in log_train times 0.1
- Focal loss would lead to nan? HOT 1
- CE+DRW and CE+CB
- Questions about the hyper-parameters for LDAM loss HOT 3
- DRW actually use Class-Balance Weight, instead of Inverse of Frequency HOT 1
- ERM Baseline HOT 1
- Codes for iNaturalist experiments? HOT 1
- Wrong implementation of focal loss HOT 2
- Points for sampler HOT 5
- Any Experiments on Face Recognition?
- Is there any pretrain model?
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