This repository is the official implementation of A Variational Approach for Learning from Positive and Unlabeled Data.
To install requirements:
pip install -r requirements.txt
To repeat experiments in the paper, run the following commands:
python run.py --dataset cifar10 --lam 0.03 --num_labeled 3000 --learning-rate 3e-5 --gpu <gpu_id>
python run.py --dataset fashionMNIST --lam 0.3 --num_labeled 3000 --learning-rate 3e-4 --gpu <gpu_id>
python run.py --dataset stl10 --lam 0.3 --num_labeled 2250 --learning-rate 1e-4 --gpu <gpu_id>
python run.py --dataset pageblocks --lam 0.0001 --num_labeled 100 --learning-rate 3e-4 --batch-size 100 --gpu <gpu_id>
python run.py --dataset grid --lam 0.1 --num_labeled 1000 --learning-rate 3e-4 --gpu <gpu_id>
python run.py --dataset avila --lam 0.1 --num_labeled 2000 --learning-rate 6e-4 --gpu <gpu_id>
Our model achieves the following performance (accuracy) on PU learing tasks of FashionMNIST, CIFAR-10 and STL-10:
| Model | FashionMNIST | CIFAR-10 | STL-10 |
|---|---|---|---|
| VPU | 92.7% | 89.5% | 79.7% |
| nnPU | 90.8% | 85.6% | 78.3% |
where nnPU is the current state-of-the-art. For more details, please refer to Table 2 and 3 in the paper.
If you find this useful, please cite
@article{chen2019variational,
title={A Variational Approach for Learning from Positive and Unlabeled Data},
author={Chen, Hui and Liu, Fangqing and Wang, Yin and Zhao, Liyue and Wu, Hao},
journal={arXiv preprint arXiv:1906.00642},
year={2019}
}