/GOLD

Mining GOLD Samples for Conditional GANs (NeurIPS 2019)

Primary LanguagePython

Mining GOLD Samples for Conditional GANs

PyTorch implementation of "Mining GOLD Samples for Conditional GANs" (NeurIPS 2019).

Run experiments

Run example re-weighting experiments

python main.py --name reweight_base --dataset mnist --epochs 20 --mode acgan_semi
python main.py --name reweight_gold --dataset mnist --epochs 20 --mode acgan_semi_gold

Run rejection sampling experiments

See rejection.ipynb

Run active learning experiments

python main.py --name active_base --dataset mnist --init_size 10 --per_size 2 --max_size 18 --mode acgan_semi --lambda_C_fake 0.01 --query_type random
python main.py --name active_gold --dataset mnist --init_size 10 --per_size 2 --max_size 18 --mode acgan_semi --lambda_C_fake 0.01 --query_type gold

Citation

If you use this code for your research, please cite our papers.

@inproceedings{
    mo2019mining,
    title={Mining GOLD Samples for Conditional GANs},
    author={Mo, Sangwoo and Kim, Chiheon and Kim, Sungwoong and Cho, Minsu and Shin, Jinwoo},
    booktitle={Advances in Neural Information Processing Systems},
    year={2019},
}