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Debiasing Vision-Language Models via Biased Prompts

Machine learning models have been shown to inherit biases from their training datasets, which can be particularly problematic for vision-language foundation models trained on uncurated datasets scraped from the internet. The biases can be amplified and propagated to downstream applications like zero-shot classifiers and text-to-image generative models. In this study, we propose a general approach for debiasing vision-language foundation models by projecting out biased directions in the text embedding. In particular, we show that debiasing only the text embedding with a calibrated projection matrix suffices to yield robust classifiers and fair generative models. The closed-form solution enables easy integration into large-scale pipelines, and empirical results demonstrate that our approach effectively reduces social bias and spurious correlation in both discriminative and generative vision-language models without the need for additional data or training.

Debiasing Vision-Language Models via Biased Prompts, Preprint 2023 [paper] <br/> Ching-Yao Chuang, Varun Jampani, Yuanzhen Li, Antonio Torralba, and Stefanie Jegelka <br/>

Prerequisites

• Python 3.6
• PyTorch 1.10.1
• PIL
• diffuser
• scikit-learn
• clip
• transformers

Code

Check the discriminative and generative folders.

Citation

If you find this repo useful for your research, please consider citing the paper

@article{chuang2023debiasing,
  title={Debiasing Vision-Language Models via Biased Prompts},
  author={Chuang, Ching-Yao and Varun, Jampani and Li, Yuanzhen and Torralba, Antonio and Jegelka, Stefanie},
  journal={arXiv preprint 2302.00070},
  year={2023}
}

For any questions, please contact Ching-Yao Chuang (cychuang@mit.edu).

Acknowledgements

The code of discriminative model is primarily inspired by the supplement of Zhang and Ré.

The code of generative model is primarily inspired by the huggingface example.