Awesome
General Greedy De-bias for Dataset Biases
This is an extention of "Greedy Gradient Ensemble for Robust Visual Question Answering" (ICCV 2021, Oral). The prerequisites can refer to GGE.
If you find this repo helpful, please cite
@article{han2021general,
title={General Greedy De-bias Learning},
author={Han, Xinzhe and Wang, Shuhui and Su, Chi and Huang, Qingming and Tian, Qi},
journal={arXiv preprint arXiv:2112.10572 },
year={2021}
}
@inproceedings{han2021greedy,
title={Greedy gradient ensemble for robust visual question answering},
author={Han, Xinzhe and Wang, Shuhui and Su, Chi and Huang, Qingming and Tian, Qi},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={1584--1593},
year={2021}
}
GGD
The core of GGD is to decompose the CE loss of GGE. The pytorch implementation is
loss = -(logits.log_softmax(-1) * labels).mean() + weight * (logits.log_softmax(-1) * bias * labels).mean()
Biased-MNIST
Biased-MNIST is produced the same with Re-bias. Run the notebook in the MNIST
folder. Modify data_label_correlation
for different bias level.
VQA-CP
Codes for VQA-CP is updated in GGE. To train a model with new GGD-CR, set import base_model_sfce
in mian.py
and run
CUDA_VISIBLE_DEVICES=0 python main.py --dataset cpv2 --mode gge_iter --debias ce_decompose --topq 1 --topv -1 --qvp 5 --output []
Adversarial SQuAD
The experiment for AdQA is provided in squad
folder. The code is modified from squad.
Acknowledgements
Some codes are modified from CSS, UpDn, squad, and Re-bias. Thanks for their open source.