/2023-CVPR-FCMI

PyTorch implementation for Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metric (CVPR 2023).

Primary LanguagePython

PyTorch implementation for

Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metric

CVPR 2023

[Paper] [Discussion] [More Information]

Introduction

FCMI framework

Requirements

  • Python 3.9
  • PyTorch 1.11.0
  • faiss
conda install -c pytorch faiss-gpu

Datasets

Color Reverse MNIST, Office-31, MTFL, and MNIST-USPS

We follow DFC to obtain datasets.

HAR

We follow DFDC to obtain dataset.

Training

Modify the ./Utils/PathPresettingOperator.get_dataset_path, then train the model(s):

# Color Reverse MNIST
python main.py --dataset ReverseMNIST --seed 0  
  
# Office-31  
python main.py --dataset Office --seed 0  
  
# MTFL  
python main.py --dataset MTFL --seed 9116  
  
# HAR  
python main.py --dataset HAR --seed 9116  
  
# MNIST-USPS  
python main.py --dataset MNISTUSPS --seed 9116

Model Zoo

The pre-trained models are available here:

Dataset Model Results
Color Reverse MNIST Model Results
Office-31 Model Results
MTFL Model Results
HAR Model Results

Download the models, then:

python main.py --dataset dataset --seed seed --resume PathToYourModel

Experiment Results:

Citation

If FCMI is useful for your research, please cite the following paper:

@inproceedings{
anonymous2023deep,
title={Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metric},
author={Anonymous},
booktitle={Conference on Computer Vision and Pattern Recognition 2023},
year={2023},
url={https://openreview.net/forum?id=A-80aauq5p}
}