Positive-Unlabeled Learning with Label Distribution Alignment (TPAMI 2023)

This is a PyTorch implementation of PULDA, which is an extension of Dist-PU.

Environments

• python>=3.7
• torch>=1.8.1
• torchvision>=0.9.1
• numpy>=1.19.2
• sklearn>=0.24.1

Data Preparation

1. Download CIFAR-10 python version (163MB) from http://www.cs.utoronto.ca/~kriz/cifar.html to your machine.
2. Decompress the downloaded file cifar-10-python.tar.gz from the first step.
3. Usually the second step would result in a new directory like '*/cifar-10-batches-py/' with files in it including:

• data_batch_[1-5]
• test_batch
• batches.meta
• readme.html

Command

python train.py --device GPUID --datapath DATAPATH

Citation

If you use the code of this repository, please cite our paper:

@ARTICLE{jiang22maxmatch,
  author={Yangbangyan Jiang and
          Qianqian Xu and
          Yunrui Zhao and
          Zhiyong Yang and
          Peisong Wen and
          Xiaochun Cao and
          Qingming Huang},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  title={Positive-Unlabeled Learning with Label Distribution Alignment},
  volume={45},
  number={12},
  pages={15345--15363},
  year={2023}
}