Effective Strategies for Bias Mitigation

Code for the CVPR paper:

Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation

Zeyu Wang, Klint Qinami, Ioannis Christos Karakozis, Kyle Genova, Prem Nair, Kenji Hata, Olga Russakovsky

@inproceedings{wang2020fair,
author = {Zeyu Wang and Klint Qinami and Ioannis Karakozis and Kyle Genova and Prem Nair and Kenji Hata and Olga Russakovsky},
title = {Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2020}
}

Requirements

• Python 3.6+
• PyTorch 1.0+
• h5py
• tensorboardX

Data Preparation

First download and unzip the CIFAR-10 and CINIC-10 by running the script download.sh

Then manually download the CelebA dataset, put Anno into data/celeba/Anno, Eval into data/celeba/Eval, put all align and cropped images to data/celeba/images

Run the preprocess_data.py to generate data for all experiments (this step involves creating h5py file for CelebA images, so would take some time 1~2 hours)

Run Experiments

To conduct experiments, run main.py with corresponding arguments (experiment specifies which experiment to run, experiment_name specifies a name to this experiment for saving the model and result). For example:

python main.py --experiment celeba_baseline --experiment_name e1 --random_seed 1

After running, the experiment result will be saved under record/experiment/experiment_name