DIM

This repository contains code to reproduce results from the paper:

Discover and Mitigate Multiple Biased Subgroups in Image Classifiers (CVPR 2024)

*Zeliang Zhang, *Mingqian Feng, Zhiheng Li, Chenliang Xu

TL; DR: we propose Decomposition, Interpretation, and Mitigation (DIM), a novel method to address a more challenging but also more practical problem of discovering multiple biased subgroups in image classifiers

Requirements

• python >= 3.6.5
• pytorch == 1.7.x
• numpy >= 1.15.4
• clip-retrieval >= 2.0

Qucik Start

There are three steps in our method, namely the D(iscovery), I(dentification), and M(itigation). We present the code implementation in discovery.py, identification.py, and mitigation.py of the DIM folder.

Citation

If you find the idea or code useful for your research, please consider citing our paper:

@inproceedings{zhang2024DIM,
  author={Zeliang Zhang and Mingqian Feng and Zhiheng Li and Chenliang Xu},
  booktitle = {The Thirty-Fourth IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  title = {Discover and Mitigate Multiple Biased Subgroups in Image Classifiers},
  year = {2024},
}

Contact

Questions and suggestions can be sent to hust0426@gmail.com.