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

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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