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DiGA: Distributionally Generative Augmentation for Fair Facial Attribute Classification (CVPR 2024)

Distributionally Generative Augmentation for Fair Facial Attribute Classification
https://arxiv.org/pdf/2403.06606

Set up

Installation

git clone https://github.com/heqianpei/DiGA.git
cd DiGA

Environment

The environment can be simply set up by Anaconda:

conda create -n DiGA python=3.8
conda activate DiGA
pip install torch==1.9.1+cu102 torchvision==0.10.1+cu102 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html
pip install matplotlib
conda install ninja
conda install -c 3dhubs gcc-5

Bias Detection

Preparation

1. Download datasets CelebA (Align&Cropped Images) and put dataset into ./data.
2. Cut the datasets to get biased datasets.

python ./data/cut_dataset.py

3. Download some pretrained models and put them in ./pretrained.

Train Biased Generative Modeling

Modify option and train_generative_model.sh and run:

bash train_generative_model.sh

Get Semantic Directions and Optimal Combination Coefficients

Run grid_search.sh to get the semantic directions, combine directions, edit some test images and choose optimal combination coefficients. The combination coefficients yielding the highest accuracy in the output are the optimal combination coefficients.

bash grid_search.sh

Bias Mitigation

1. Change parameter $choose$ to optimal combination coefficient from grid_search.sh output.
2. Run train_classifier.sh to train fair encoder and train fair classifier.

bash train_classifier.sh

Citation

If you find this work useful for your research, please cite:

@inproceedings{
  title={Distributionally Generative Augmentation for Fair Facial Attribute Classification},
  author={Fengda Zhang, Qianpei He, Kun Kuang, Jiashuo Liu, Long Chen, Chao Wu, Jun Xiao, Hanwang Zhang},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2024}
}

Acknowledgement

Thanks to Tengfei-Wang for sharing their code.