Comments (3)
We didn't MixMatch in this setting. One could see a multi-label classification loss as multiple binary losses to model the problem in a compatible way with MixMatch. I guess I can't really give more advice than that and experimenting with the code on your data.
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Thank you for your reply. I used my own multi classification dataset (not multi label classification) to train mixmatch. The first few rounds of training worked well,after the first few epochs of network learning, the loss began to rise, the precision decreased, and many parameters were adjusted. Could this be due to the label prediction error of unlabeled samples, which led to the worse training effect of the model?
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It could be a lot of things, it's hard for me to tell. This repository is mostly to show how to reproduce the results achieved in the paper.
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Related Issues (20)
- When will Remixmatch (ICLR'20) be available? HOT 3
- A question about "post_ops" in mixmatch.py HOT 2
- Implemented on other models HOT 1
- What are the most important things to reproduce the result on my own dataset? HOT 2
- Use MixMatch on tabular data. HOT 6
- A question about lambda_u HOT 2
- Is there any reason why you chose to use Beta Distribution? HOT 1
- Reason for ramping up weight of unlabelled loss function(lambda_u). HOT 3
- Comparison of fully supervised models with MixMatch. HOT 2
- How to chose total number of training steps HOT 5
- how to save the train and test accuracies to disk HOT 2
- question about mixmatch/scripts/create_split.py line113-130
- Working with higher resolution images HOT 1
- what is the proper behavior of consistency loss HOT 1
- how to recover performance when doing evaluation HOT 4
- why not using dropout in the wide resnet as done in the wide resnet paper? HOT 4
- In your implementation of Mean teacher, isn't the student model and the teacher model the same? HOT 1
- ModuleNotFoundError: No module named 'libml' HOT 1
- Project dependencies may have API risk issues HOT 1
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