Code for CoMatch: Semi-supervised Learning with Contrastive Graph Regularization

Related tags

Deep Learning semi-supervised-learning representation-learning self-supervised-learning contrastive-learning
Overview

CoMatch: Semi-supervised Learning with Contrastive Graph Regularization (Salesforce Research)

This is a PyTorch implementation of the CoMatch paper [Blog]:

@article{CoMatch,
	title={Semi-supervised Learning with Contrastive Graph Regularization},
	author={Junnan Li and Caiming Xiong and Steven C.H. Hoi},
	journal={arXiv preprint arXiv:2011.11183},
	year={2020}
}

Requirements:

  • PyTorch ≥ 1.4
  • pip install tensorboard_logger
  • download and extract cifar-10 dataset into ./data/

To perform semi-supervised learning on CIFAR-10 with 4 labels per class, run:

python Train_CoMatch.py --n-labeled 40 --seed 1

The results using different random seeds are:

seed 1 2 3 4 5 avg
accuracy 93.71 94.10 92.93 90.73 93.97 93.09

ImageNet

For ImageNet experiments, see ./imagenet/

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Comments
  • Codes for Dataloader of STL Dataset

    Codes for Dataloader of STL Dataset

    Thanks a lot for sharing your clear codes. I try to train the models on STL dataset but don't find the dataloader of STL suitable for SSL setting. Would you like to share the codes of STL Dataloader with randomly splitting labeled&unlabeled data?

    opened by QiushiYang 4
  • How to improve performance

    How to improve performance

    the idea in your paper is amazing，great truths are all simple. I have the following questions: 1、Does a stronger data enhancement method, such as RandomAugment, improve the performance? 2、To improve the performance, is necessary expend the size of distribution alignment ? 3、how to adjust the memory bank size when more label data is used in ImageNet ,such as 20% of ImageNet? 4、If use 20% of ImageNet data as a label, what are the recommendations for other hyperparameters? 5、From your point of view, what are the main challenges in achieving full oversight with 20% of ImageNet data?

    opened by happyxuwork 3
  • Questions about distribution align codes

    Questions about distribution align codes

    Thanks for sharing your clean codes. While I find that the codes of distribution alignment (DA) only calculate: q = Norm(q/mean(p(y))), without multiplying labeled data distribution p(y) and further sharpening, which seems not same as the DA introduced in ReMixMatch. Wonder why don't use the standard DA to train models? Is the simple way you used better for SSL models' performance?

    opened by QiushiYang 1
  • Several Questions

    Several Questions

    Hi, Thank you for your great work, I am new to this area, I have the following questions: 1、How much improvements can be made by the branch of Graph-based contrastive learning? I can not see that ablation experiments in the paper. I'm interested in this part and would also like to know the performance improvements that might be useful for my task. 2、Does the Co-match method fit for multi-label classification tasks？

    Once again, thank you for your nice work and clean code! Looking forward to your reply. Best regards! Tan

    opened by myt889 2
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