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},
}