Comments (3)
Good question. I think in this case, your actual problem is to apply MoCo on an imbalanced dataset. There could be many factors. One possible explanation is on hyper-parameters --- for example, the number of pre-training epochs, as I only trained for 200 epochs, longer training might bring better performance. Also, during the fine-tuning stage on linear classifier, you might want to follow the hyper-parameter in MoCo's repo, i.e., the learning rate (they usually use very large lr, e.g., 30, for the linear classifier).
And indeed, the performance might be actually reasonable, indicating that current contrastive self-supervised learning might have large performance drop when facing imbalanced data. This problem is an independent problem, and might be intereting to the self-supervised learning community.
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thank you so much!
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Hi, I'm very interested in this paper. Recently, I'm trying to reproduce the results on ImageNet-LT with this code. However, I found that the loss value can only decrease from 9.5 to 6.9 for the moco pretraining. Is it correct? Can you post your training log for reference ? Thank you very much.
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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
- 请教大大一个问题:FileNotFoundError: [Errno 2] No such file or directory: './data/ti_80M_selected.pickle' HOT 1
- Some problems about the assumption in the papaer. HOT 2
- The pretrained models of "self" can not be open,can you solve it,pealse? HOT 3
- 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
- 训练自己数据集 HOT 5
- 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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