Comments (5)
Hi @SparkParis @ShirleyHe2020 - thanks for your interest! General details are indicated as above:
- Semi-supervised: You just need to first collect (relevant) unlabeled data. As indicated by our results, as long as the data relevance is high, even the unlabeled data is also (highly) imbalanced, it can still (almost always) improve the imbalanced learning results. Then you can just use self-training, or other semi-supervised techniques (advanced methods such as VAT/MT will achieve better results, see our Appendix E.1).
- Self-supervised: Depending on your data format, you can choose any self-supervised learning methods that are suitable for your own data. All you need is to first do self-supervised pre-training without using imbalanced labels. Then you can use any base training method to do supervised training. As our paper shows, it can consistently improve the results, regardless of the base training techniques.
Let me know if you have any questions.
from imbalanced-semi-self.
Hi, thank you for your interest. To apply on your own datasets:
- Semi-supervised: You just need to first collect (relevant) unlabeled data. As indicated by our results, as long as the data relevance is high, even the unlabeled data is also (highly) imbalanced, it can still (almost always) improve the imbalanced learning results. Then you can just use self-training, or other semi-supervised techniques (advanced methods such as VAT/MT will achieve better results, see our Appendix E.1).
- Self-supervised: Depending on your data format, you can choose any self-supervised learning methods that are suitable for your own data. All you need is to first do self-supervised pre-training without using imbalanced labels. Then you can use any base training method to do supervised training. As our paper shows, it can consistently improve the results, regardless of the base training techniques.
from imbalanced-semi-self.
please give me a detail to train my databases
from imbalanced-semi-self.
thank you
from imbalanced-semi-self.
Love your idea .Can you plz share more details about how to train custom datasets?
from imbalanced-semi-self.
Related Issues (20)
- Where can I setting the CE(Uniform) and CE(Balanced) ? HOT 1
- About the Proof of Theorem1 HOT 3
- How to apply to traditional ML techniques such as lightgbm? HOT 1
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- Some problems about the assumption in the papaer. HOT 2
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- What is the intended learning rate schedule? HOT 1
- Can't achieve the given performance: ResNet-50 + SSP+CE(Uniform) for imageNet-LT HOT 3
- Have you ever tried "Semi-Supervised Imbalanced Learning on ImageNet-LT"? HOT 2
- moco on cifar dataset HOT 1
- command to get a base classifier in semi-supervised learning HOT 1
- 是否有在多分类分割问题上衡量这个方法呢? HOT 2
- error python pretrain_rot.py --dataset cifar10 --imb_factor 0.01 HOT 4
- How to get the image in the readme HOT 4
- 你好,半监督的伪标签没有经过置信度筛选的吗? HOT 1
- Questions about self-supervised learning on cifar10 HOT 7
- Can it be used to solve the unbalanced problem of supervised learning? And How? HOT 3
- What's the required hardware to reproduce the result? HOT 1
- Why use 5 times more unlabeled data? HOT 1
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